3,311 results on '"Neubauer, Stefan"'
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2. LegoNet: Alternating Model Blocks for Medical Image Segmentation
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Sobirov, Ikboljon, Xie, Cheng, Siddique, Muhammad, Patel, Parijat, Chan, Kenneth, Halborg, Thomas, Kotanidis, Christos, Fatima, Zarqiash, West, Henry, Channon, Keith, Neubauer, Stefan, Antoniades, Charalambos, and Yaqub, Mohammad
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters. To leverage the benefits of different architectural designs (e.g. CNNs and ViTs), we propose to alternate structurally different types of blocks to generate a new architecture, mimicking how Lego blocks can be assembled together. Using two CNN-based and one SwinViT-based blocks, we investigate three variations to the so-called LegoNet that applies the new concept of block alternation for the segmentation task in medical imaging. We also study a new clinical problem which has not been investigated before, namely the right internal mammary artery (RIMA) and perivascular space segmentation from computed tomography angiography (CTA) which has demonstrated a prognostic value to major cardiovascular outcomes. We compare the model performance against popular CNN and ViT architectures using two large datasets (e.g. achieving 0.749 dice similarity coefficient (DSC) on the larger dataset). We evaluate the performance of the model on three external testing cohorts as well, where an expert clinician made corrections to the model segmented results (DSC>0.90 for the three cohorts). To assess our proposed model for suitability in clinical use, we perform intra- and inter-observer variability analysis. Finally, we investigate a joint self-supervised learning approach to assess its impact on model performance. The code and the pretrained model weights will be available upon acceptance., Comment: 12 pages, 5 figures, 4 tables
- Published
- 2023
3. NHS Health Check attendance is associated with reduced multiorgan disease risk: a matched cohort study in the UK Biobank
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McCracken, Celeste, Raisi-Estabragh, Zahra, Szabo, Liliana, Robson, John, Raman, Betty, Topiwala, Anya, Roca-Fernández, Adriana, Husain, Masud, Petersen, Steffen E., Neubauer, Stefan, and Nichols, Thomas E.
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- 2024
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4. Cognitive and psychiatric symptom trajectories 2–3 years after hospital admission for COVID-19: a longitudinal, prospective cohort study in the UK
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Lone, Nazir, Baillie, Kenneth, Pairo-Castineira, Erola, Avramidis, Nikos, Wain, Louise, Guillen-Guio, Beatriz, Leavy, Olivia, Jones, S, Armstrong, Lisa, Hairsine, Brigid, Henson, Helen, Kurasz, Claire, Shaw, Alison, Shenton, Liz, Dobson, Hannah, Dell, Amanda, Fairbairn, Sara, Hawkings, Nancy, Haworth, Jill, Hoare, Michaela, Lewis, Victoria, Lucey, Alice, Mallison, Georgia, Nassa, Heeah, Pennington, Chris, Price, Andrea, Price, Claire, Storrie, Andrew, Willis, Gemma, Young, Susan, Poinasamy, Krisnah, Walker, Samantha, Jarrold, Ian, Rawlik, Konrad, Sanderson, Amy, Chong-James, K, David, C, James, W Y, Pfeffer, Paul, Zongo, O, Martineau, Adrian, Manisty, C, Armour, Cherie, Brown, Vanessa, Busby, John, Connolly, Bronwen, Craig, Thelma, Drain, Stephen, Heaney, Liam, King, Bernie, Magee, Nick, Major, E, McAulay, Danny, McGarvey, Lorcan, McGinness, Jade, Peto, Tunde, Stone, Roisin, Bolger, Annette, Davies, Ffyon, Haggar, Ahmed, Lewis, Joanne, Lloyd, Arwel, Manley, R, McIvor, Emma, Menzies, Daniel, Roberts, K, Saxon, W, Southern, David, Subbe, Christian, Whitehead, Victoria, Bularga, Anda, Mills, Nicholas, Dawson, Joy, El-Taweel, Hosni, Robinson, Leanne, Brear, Lucy, Regan, Karen, Saralaya, Dinesh, Storton, Kim, Amoils, Shannon, Bermperi, Areti, Cruz, Isabel, Dempsey, K, Elmer, Anne, Fuld, Jonathon, Jones, H, Jose, Sherly, Marciniak, Stefan, Parkes, M, Ribeiro, Carla, Taylor, Jessica, Toshner, Mark, Watson, L, Worsley, J, Broad, Lauren, Evans, Teriann, Haynes, Matthew, Jones, L, Knibbs, Lucy, McQueen, Alison, Oliver, Catherine, Paradowski, Kerry, Sabit, Ramsey, Williams, Jenny, Jones, Ian, Milligan, Lea, Harris, Edward, Sampson, Claire, Davies, Ellie, Evenden, Cerys, Hancock, Alyson, Hancock, Kia, Lynch, Ceri, Rees, Meryl, Roche, Lisa, Stroud, Natalie, Thomas-Woods, T, Heller, Simon, Chalder, Trudie, Shah, Kamini, Robertson, Elizabeth, Young, Bob, Babores, Marta, Holland, Maureen, Keenan, Natalie, Shashaa, Sharlene, Wassall, Helen, Austin, Liam, Beranova, Eva, Cosier, Tracey, Deery, Joanne, Hazelton, Tracy, Price, Carly, Ramos, Hazel, Solly, Reanne, Turney, Sharon, Weston, Heather, Coughlan, Eamon, Ralser, Markus, Pearce, Lorraine, Pugmire, S, Stoker, Wendy, Wilson, Ann, McCormick, W, Fraile, Eva, Ugoji, Jacinta, Aguilar Jimenez, Laura, Arbane, Gill, Betts, Sarah, Bisnauthsing, Karen, Dewar, A, Hart, Nicholas, Kaltsakas, G, Kerslake, Helen, Magtoto, Murphy, Marino, Philip, Martinez, L M, Ostermann, Marlies, Rossdale, Jennifer, Solano, Teresa, Alvarez Corral, Maria, Arias, Ava Maria, Bevan, Emily, Griffin, Denise, Martin, Jane, Owen, J, Payne, Sheila, Prabhu, A, Reed, Annabel, Storrar, Will, Williams, Nick, Wrey Brown, Caroline, Burdett, Tracy, Featherstone, James, Lawson, Cathy, Layton, Alison, Mills, Clare, Stephenson, Lorraine, Ellis, Yvette, Atkin, Paul, Brindle, K, Crooks, Michael, Drury, Katie, Easom, Nicholas, Flockton, Rachel, Holdsworth, L, Richards, A, Sykes, D L, Thackray-Nocera, Susannah, Wright, C, Coetzee, S, Davies, Kim, Hughes, Rachel 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Zunaira, Odell, Natasha, Osbourne, Rebecca, Piper Hanley, Karen, Radhakrishnan, K, Stockdale, Sue, Kabir, Thomas, Scott, Janet, Stewart, Iain, Openshaw, Peter, Burn, David, Ayoub, A, Brown, J, Burns, G, Davies, Gareth, De Soyza, Anthony, Echevarria, Carlos, Fisher, Helen, Francis, C, Greenhalgh, Alan, Hogarth, Philip, Hughes, Joan, Jiwa, Kasim, Jones, G, MacGowan, G, Price, D, Sayer, Avan, Simpson, John, Tedd, H, Thomas, S, West, Sophie, Witham, M, Wright, S, Young, A, McMahon, Michael, Neill, Paula, Anderson, David, Basu, Neil, Bayes, Hannah, Brown, Ammani, Dougherty, Andrew, Fallon, K, Gilmour, L, Grieve, D, Mangion, K, Morrow, A, Sykes, R, Berry, Colin, McInnes, I B, Scott, Kathryn, Barrett, Fiona, Donaldson, A, Sage, Beth, Bell, Murdina, Brown, Angela, Hamil, R, Leitch, Karen, Macliver, L, Patel, Manish, Quigley, Jackie, Smith, Andrew, Welsh, B, Choudhury, Gaunab, Clohisey, S, Deans, Andrew, Docherty, Annemarie, Furniss, J, Harrison, Ewen, Kelly, S, Sheikh, Aziz, Chalmers, James, Connell, David, Deas, C, Elliott, Anne, George, J, Mohammed, S, Rowland, J, Solstice, AR, Sutherland, Debbie, Tee, Caroline, Bunker, Jenny, Gill, Rhyan, Nathu, Rashmita, Holmes, Katie, Adamali, H, Arnold, David, Barratt, Shaney, Dipper, A, Dunn, Sarah, Maskell, Nick, Morley, Anna, Morrison, Leigh, Stadon, Louise, Waterson, Samuel, Welch, H, Jayaraman, Bhagy, Light, Tessa, Vogiatzis, Ioannis, Almeida, Paula, Bolton, Charlotte, Hosseini, Akram, Matthews, Laura, Needham, Robert, Shaw, Karen, Thomas, Andrew, Bonnington, J, Chrystal, Melanie, Dupont, Catherine, Greenhaff, Paul, Gupta, Ayushman, Jang, W, Linford, S, Nikolaidis, Athanasios, Prosper, Sabrina, Burns, A, Kanellakis, N, Ferreira, V, Nikolaidou, C, Xie, C, Ainsworth, Mark, Alamoudi, Asma, Bloss, Angela, Carter, Penny, Cassar, M, Chen, Jin, Conneh, Florence, Dong, T, Evans, Ranuromanana, Fraser, Emily, Geddes, John, Gleeson, F, Harrison, Paul, Havinden-Williams, May, Ho, Ling Pei, Jezzard, P, Koychev, Ivan, Kurupati, Prathiba, 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Roger, Tinker, N, Turner, Kim, Turton, Helena, Wade, Phillip, Walker, S, Watson, James, Wilson, Imogen, Zawia, Amira, Allsop, Lynne, Bennett, Kaytie, Buckley, Phil, Flynn, Margaret, Gill, Mandy, Goodwin, Camelia, Greatorex, M, Gregory, Heidi, Heeley, Cheryl, Holloway, Leah, Holmes, Megan, Hutchinson, John, Kirk, Jill, Lovegrove, Wayne, Sewell, Terri Ann, Shelton, Sarah, Sissons, D, Slack, Katie, Smith, Susan, Sowter, D, Turner, Sarah, Whitworth, V, Wynter, Inez, Tomlinson, Johanne, Warburton, Louise, Painter, Sharon, Palmer, Sue, Redwood, Dawn, Tilley, Jo, Vickers, Carinna, Wainwright, Tania, Breen, G, Hotopf, M, Aul, Raminder, Forton, D, Ali, Mariam, Dunleavy, A, Mencias, Mark, Msimanga, N, Samakomva, T, Siddique, Sulman, Tavoukjian, Vera, Teixeira, J, Ahmed, Rubina, Francis, Richard, Connor, Lynda, Cook, Amanda, Davies, Gwyneth, Rees, Tabitha, Thaivalappil, Favas, Thomas, Caradog, McNarry, M, Williams, N, Lewis, Keir, Coulding, Martina, Jones, Heather, Kilroy, Susan, McCormick, 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Karen, Jackson, T, Kamwa, Vicky, Lewis, D, Lord, Janet, Madathil, S, McGee, C, Mcgee, K, Neal, Aoife, Newton-Cox, Alex, Nyaboko, Joseph, Parekh, Dhruv, Peterkin, Z, Qureshi, H, Ratcliffe, Liz, Sapey, Elizabeth, Short, J, Soulsby, Tracy, Stockley, J, Suleiman, Zehra, Thompson, Tamika, Ventura, Maximina, Walder, Sinead, Welch, Carly, Wilson, Daisy, Yasmin, S, Yip, Kay Por, Chaudhuri, N, Childs, Caroline, Djukanovic, R, Fletcher, S, Harvey, Matt, Jones, Mark, Marouzet, Elizabeth, Marshall, B, Samuel, Reena, Sass, T, Wallis, Tim, Wheeler, Helen, Steeds, R, Beckett, Paul, Dickens, Caroline, Nanda, Uttam, Aljaroof, M, Armstrong, Natalie, Arnold, H, Aung, Hnin, Bakali, Majda, Bakau, M, Baldry, E, Baldwin, Molly, Bourne, Charlotte, Bourne, Michelle, Brightling, Chris, Brunskill, Nigel, Cairns, P, Carr, Liesel, Charalambou, Amanda, Christie, C, Davies, Melanie, Daynes, Enya, Diver, Sarah, Dowling, Rachael, Edwards, Sarah, Edwardson, C, Elneima, Omer, Evans, H, Evans, Rachael, Finch, J, Glover, Sarah, Goodman, Nicola, Gooptu, Bibek, Greening, Neil, Hadley, Kate, Haldar, Pranab, Hargadon, Beverley, Harris, Victoria, Houchen-Wolloff, Linzy, Ibrahim, W, Ingram, L, Khunti, Kamlesh, Lea, A, Lee, D, McAuley, Hamish, McCann, Gerry, McCourt, P, Mcnally, Teresa, Mills, George, Monteiro, Will, Pareek, Manish, Parker, S, Prickett, Anne, Qureshi, I N, Rowland, A, Russell, Richard, Sereno, Marco, Shikotra, Aarti, Siddiqui, Salman, Singapuri, Ananga, Singh, Sally, Skeemer, J, Soares, M, Stringer, E, Thornton, T, Tobin, Martin, Ward, T J C, Woodhead, F, Yates, Tom, Yousuf, A J, Broome, Mattew, McArdle, Paul, Thickett, David, Upthegrove, Rachel, Wilkinson, Dan, Moss, Paul, Wraith, David, Evans, Jonathon, Bullmore, Ed, Heeney, Jonathon, Langenberg, Claudia, Schwaeble, William, Summers, Charlotte, Weir McCall, J, Adeloye, Davies, Newby, D E, Pius, Riinu, Rudan, Igor, Shankar-Hari, Manu, Sudlow, Catherine, Thorpe, Mat, Walmsley, Sarah, Zheng, Bang, Allan, Louise, Ballard, Clive, McGovern, Andrew, Dennis, J, Cavanagh, Jonathon, MacDonald, S, O'Donnell, Kate, Petrie, John, Sattar, Naveed, Spears, Mark, Guthrie, Elspeth, Henderson, Max, Allen, Richard, Bingham, Michelle, Brugha, Terry, Finney, Selina, Free, Rob, Jones, Don, Lawson, Claire, Lucy, Gardiner, Moss, Alistair, Mukaetova-Ladinska, Elizabeta, Novotny, Petr, Overton, Charlotte, Pearl, John, Plekhanova, Tatiana, Richardson, M, Samani, Nilesh, Sargant, Jack, Sharma, M, Steiner, Mike, Taylor, Chris, Terry, Sarah, Tong, C, Turner, E, Wormleighton, J, Zhao, Bang, Ntotsis, Kimon, Saunders, Ruth, Lozano-Rojas, Daniel, Goemans, Anne, Cuthbertson, D, Kemp, G, McArdle, Anne, Michael, Benedict, Reynolds, Will, Spencer, Lisa, Vinson, Ben, Ashworth, M, Abel, Kathryn, Chinoy, H, Deakin, Bill, Harvie, M, Miller, C A, Stanel, Stefan, Barran, Perdita, Trivedi, Drupad, McAllister-Williams, Hamish, Paddick, Stella-Maria, Rostron, Anthony, Taylor, John Paul, Baguley, David, Coleman, Chris, Cox, E, Fabbri, Laura, Francis, Susan, Hall, Ian, Hufton, E, Johnson, Simon, Khan, Fasih, Kitterick, Paaig, Morriss, Richard, Selby, Nick, Wright, Louise, Antoniades, Charalambos, Bates, A, Beggs, M, Bhui, Kamaldeep, Breeze, Katie, Channon, K M, Clark, David, Fu, X, Husain, Masud, Li, X, Lukaschuk, E, McCracken, Celeste, McGlynn, K, Menke, R, Motohashi, K, Nichols, T E, Ogbole, Godwin, Piechnik, S, Propescu, I, Propescu, J, Samat, A A, Sanders, Z B, Sigfrid, Louise, Webster, M, Kingham, Lucy, Klenerman, Paul, Lamlum, Hanan, Taquet, Maxime, Carson, G, Finnigan, L, Saunders, Laura, Wild, James, Calder, P C, Huneke, Nathan, Simons, Gemma, Baldwin, David, Bain, Steve, Daines, Luke, Bright, E, Crisp, P, Dharmagunawardena, Ruvini, Stern, M, Bailey, Elisabeth, Reddington, Anne, Wight, Andrew, Ashish, A, Cooper, Josh, Robinson, Emma, Broadley, Andrew, Barman, Laura, Brookes, Claire, Elliott, K, Griffiths, L, Guy, Zoe, Howard, Kate, Ionita, Diana, Redfearn, Heidi, Sarginson, Carol, Turnbull, Alison, Skorniewska, Zuzanna, De Deyn, Thomas, Hampshire, Adam, Trender, William R, Hellyer, Peter J, Chalmers, James D, Ho, Ling-Pei, Leavy, Olivia C, Richardson, Matthew, McAuley, Hamish J C, Singapuri, Amisha, Saunders, Ruth M, Harris, Victoria C, Greening, Neil J, Mansoori, Parisa, Harrison, Ewen M, Docherty, Annemarie B, Lone, Nazir I, Quint, Jennifer, Brightling, Christopher E, Wain, Louise V, Evans, Rachael A, Geddes, John R, and Harrison, Paul J
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- 2024
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5. Liraglutide Improves Myocardial Perfusion and Energetics and Exercise Tolerance in Patients With Type 2 Diabetes
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Chowdhary, Amrit, Thirunavukarasu, Sharmaine, Joseph, Tobin, Jex, Nicholas, Kotha, Sindhoora, Giannoudi, Marilena, Procter, Henry, Cash, Lizette, Akkaya, Sevval, Broadbent, David, Xue, Hui, Swoboda, Peter, Valkovič, Ladislav, Kellman, Peter, Plein, Sven, Rider, Oliver J., Neubauer, Stefan, Greenwood, John P., and Levelt, Eylem
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- 2024
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6. Predictive Performance of Cardiovascular Risk Scores in Cancer Survivors From the UK Biobank
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McCracken, Celeste, Condurache, Dorina-Gabriela, Szabo, Liliana, Elghazaly, Hussein, Walter, Fiona M., Mead, Adam J., Chakraverty, Ronjon, Harvey, Nicholas C., Manisty, Charlotte H., Petersen, Steffen E., Neubauer, Stefan, and Raisi-Estabragh, Zahra
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- 2024
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7. Incidence of diabetes after SARS-CoV-2 infection in England and the implications of COVID-19 vaccination: a retrospective cohort study of 16 million people
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Al Arab, Marwa, Almaghrabi, Fatima, Andrews, Colm, Badrick, Ellena, Baz, Sarah, Beckford, Chelsea, Berman, Samantha, Bolton, Tom, Booth, Charlotte, Bowyer, Ruth, Boyd, Andy, Bridger-Staatz, Charis, Brophy, Sinead, Campbell, Archie, Campbell, Kirsteen C, Carnemolla, Alisia, Carpentieri, Jd, Cezard, Genevieve, Chaturvedi, Nishi, Cheetham, Nathan, Costello, Ruth, Cowling, Thomas, Crane, Matthew, Cuitun Coronado, Jose Ignacio, Curtis, Helen, Denaxas, Spiros, Denholm, Rachel, Di Gessa, Giorgio, Dobson, Richard, Douglas, Ian, Evans, Katharine M, Fang, Chao, Ferreira, Vanessa, Finnigan, Lucy, Fisher, Louis, Flaig, Robin, Folarin, Amos, Forbes, Harriet, Foster, Diane, Fox, Laura, Freydin, Maxim, Garcia, Paz, Gibson, Andy, Glen, Fiona, Goldacre, Ben, Goncalves Soares, Ana, Greaves, Felix, Green, Amelia, Green, Mark, Green, Michael, Griffith, Gareth, Hamill Howes, Lee, Hamilton, Olivia, Herbet, Annie, Herrett, Emily, Hopcroft, Lisa, Horne, Elsie, Hou, Bo, Hughes, Alun, Hulme, William, Huntley, Lizzie, Ip, Samantha, Jacques, Wels, Jezzard, Peter, Jones, Louise, Kanagaratnam, Arun, Karthikeyan Suseeladevi, Arun, Katikireddi, Vittal, Kellas, John, Kennedy, Jonathan I, Kibble, Milla, Knight, Rochelle, Knueppel, Anika, Kopasker, Daniel, Kromydas, Theocharis, Kwong, Alex, Langan, Sinead, Lemanska, Agnieszka, Lukaschuk, Elena, Mackenna, Brain, Macleod, John, Maddock, Jane, Mahalingasivam, Viyaasan, Mansfield, Kathryn, McArdle, Fintan, McCartney, Daniel, McEachan, Rosie, McElroy, Eoin, McLachlan, Stela, Mitchell, Ruth, Moltrecht, Bettina, Morley, Jess, Nab, Linda, Neubauer, Stefan, Nigrelli, Lidia, North, Teri, Northstone, Kate, Oakley, Jacqui, Palmer, Tom, Park, Chloe, Parker, Michael, Parsons, Sam, Patalay, Praveetha, Patel, Kishan, Perez-Reche, Francisco, Piechnik, Stefan, Piehlmaier, Dominik, Ploubidis, George, Rafeti, Elena, Raman, Betty, Ranjan, Yatharth, Rapala, Alicja, Rhead, Rebecca, Roberts, Amy, Sampri, Alexia, Sanders, Zeena-Britt, Santorelli, Gillian, Saunders, Laura C, Shah, Anoop, Shah, Syed Ahmar, Sharp, Steve, Shaw, Richard, Sheard, Laura, Sheikh, Aziz, Silverwood, Richard, Smeeth, Liam, Smith, Stephen, Stafford, Jean, Steptoe, Andrew, Sterne, Jonathan, Steves, Claire, Stewart, Callum, Taylor, Kurt, Tazare, John, Teece, Lucy, Thomas, Richard, Thompson, Ellen, Tilling, Kate, Timpson, Nicholas, Tomlinson, Laurie, Toms, Renin, Tunnicliffe, Elizabeth, Turner, Emma L, Walker, Alex, Walker, Venexia, Walter, Scott, Wang, Kevin, Wei, Yinghui, Whitehorn, Rebecca, Wielgoszewska, Bozena, Wild, James M, Willan, Kathryn, Willans, Robert, Williams, Dylan, Wong, Andrew, Wood, Angela, Woodward, Hannah, Wright, John, Yang, Tiffany, Zaninotto, Paola, Zheng, Bang, Zhu, Jingmin, Eastwood, Sophie, Horne, Elsie M F, Massey, Jon, Hopcroft, Lisa E M, Cuitun Coronado, Jose, Davy, Simon, Dillingham, Iain, Morton, Caroline, and Sterne, Jonathan A C
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- 2024
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8. Diagnostic utility of electrocardiogram for screening of cardiac injury on cardiac magnetic resonance in post-hospitalised COVID-19 patients: a prospective multicenter study
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Samat, Azlan Helmy Abd, Cassar, Mark P., Akhtar, Abid M., McCracken, Celeste, Ashkir, Zakariye M., Mills, Rebecca, Moss, Alastair J., Finnigan, Lucy E.M., Lewandowski, Adam J., Mahmod, Masliza, Ogbole, Godwin I., Tunnicliffe, Elizabeth M., Lukaschuk, Elena, Piechnik, Stefan K., Ferreira, Vanessa M., Nikolaidou, Chrysovalantou, Rahman, Najib M., Ho, Ling-Pei, Harris, Victoria C., Singapuri, Amisha, Manisty, Charlotte, O'Regan, Declan P., Weir-McCall, Jonathan R., Steeds, Richard P., LLM, Krisnah Poinasamy, Cuthbertson, Dan J., Kemp, Graham J., Horsley, Alexander, Miller, Christopher A., O'Brien, Caitlin, Chiribiri, Amedeo, Francis, Susan T., Chalmers, James D., Plein, Sven, Poener, Ana-Maria, Wild, James M., Treibel, Thomas A., Marks, Michael, Toshner, Mark, Wain, Louise V., Evans, Rachael A., Brightling, Christopher E., Neubauer, Stefan, McCann, Gerry P., and Raman, Betty
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- 2024
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9. Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study
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Thomas, Sheena, Denton, Jon, Farral, Robyn, Taylor, Carolyn, Qin, Wendy, Kasongo, Mary, Anthony, Susan, Banning, Adrian, Xie, Cheng, Kharbanda, Rajesh K, Pritchard, Amy, Halborg, Thomas, Syed, Nigar, Fry, Sam, Mathers, Chris, Rose, Anne, Hudson, George, Bajaj, Amrita, Das, Intrajeet, Deshpande, Aparna, Rao, Praveen, Lawday, Dan, Mirsadraee, Saeed, Hudson, Benjamin, Berry, Colin, Marwan, Mohamed, Maurovich-Horvat, Pál, He, Guo-Wei, Lin, Wen-Hua, Fan, Li-Juan, Takahashi, Naohiko, Kondo, Hidekazu, Dai, Neng, Ge, Junbo, Koo, Bon-Kwon, Guglielmo, Marco, Pontone, Gianluca, Huck, Daniel, Benedek, Theodora, Rajani, Ronak, Vilic, Dijana, Aljazzaf, Haleema, Mun, Mak S, Benedetti, Giulia, Preston, Rebecca L, Raisi-Estabragh, Zahra, Connolly, Derek L, Sharma, Vinoda, Grenfell, Rebecca, Bradlow, William, Schmitt, Matthias, Serfaty, Fabiano, Gottlieb, Ilan, Neves, Mario FT, Newby, David E, Dweck, Marc R, Hatem, Stéphane, Redheuil, Alban, Benetos, Georgios, Beer, Meinrad, Granillo, Gastón AR, Selvanayagam, Joseph, Lopez-Jimenez, Francisco, De Bosscher, Ruben, Tavildari, Alain, Figtree, Gemma, Danad, Ibrahim, Shantouf, Ronney, Kietselaer, Bas, Tousoulis, Dimitris, Dangas, George, Mehta, Nehal N, Kontanidis, Christos, Kunadian, Vijay, Fairbairn, Timothy A, Chan, Kenneth, Wahome, Elizabeth, Tsiachristas, Apostolos, Antonopoulos, Alexios S, Patel, Parijat, Lyasheva, Maria, Kingham, Lucy, West, Henry, Oikonomou, Evangelos K, Volpe, Lucrezia, Mavrogiannis, Michail C, Nicol, Edward, Mittal, Tarun K, Kotronias, Rafail A, Adlam, David, Modi, Bhavik, Rodrigues, Jonathan, Screaton, Nicholas, Kardos, Attila, Greenwood, John P, Sabharwal, Nikant, De Maria, Giovanni Luigi, Munir, Shahzad, McAlindon, Elisa, Sohan, Yogesh, Tomlins, Pete, Siddique, Muhammad, Kelion, Andrew, Shirodaria, Cheerag, Pugliese, Francesca, Petersen, Steffen E, Blankstein, Ron, Desai, Milind, Gersh, Bernard J, Achenbach, Stephan, Libby, Peter, Neubauer, Stefan, Channon, Keith M, Deanfield, John, and Antoniades, Charalambos
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- 2024
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10. Safety and Efficacy of Metabolic Modulation With Ninerafaxstat in Patients With Nonobstructive Hypertrophic Cardiomyopathy
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Maron, Martin S., Mahmod, Masliza, Abd Samat, Azlan Helmy, Choudhury, Lubna, Massera, Daniele, Phelan, Dermot M.J., Cresci, Sharon, Martinez, Matthew W., Masri, Ahmad, Abraham, Theodore P., Adler, Eric, Wever-Pinzon, Omar, Nagueh, Sherif F., Lewis, Gregory D., Chamberlin, Paul, Patel, Jai, Yavari, Arash, Dehbi, Hakim-Moulay, Sarwar, Rizwan, Raman, Betty, Valkovič, Ladislav, Neubauer, Stefan, Udelson, James E., and Watkins, Hugh
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- 2024
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11. Synergistic effect on cardiac energetics by targeting the creatine kinase system: in vivo application of high-resolution 31P-CMRS in the mouse
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Maguire, Mahon L., McAndrew, Debra J., Lake, Hannah A., Ostrowski, Philip J., Zervou, Sevasti, Neubauer, Stefan, Lygate, Craig A., and Schneider, Jurgen E.
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- 2023
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12. Editorial Expression of Concern: Splenic T1-mapping: a novel quantitative method for assessing adenosine stress adequacy for cardiovascular magnetic resonance
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Liu, Alexander, Wijesurendra, Rohan S., Ariga, Rina, Mahmod, Masliza, Levelt, Eylem, Greiser, Andreas, Petrou, Mario, Krasopoulos, George, Forfar, John C., Kharbanda, Rajesh K., Channon, Keith M., Neubauer, Stefan, Piechnik, Stefan K., and Ferreira, Vanessa M.
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- 2023
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13. Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation
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Puyol-Anton, Esther, Ruijsink, Bram, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Razavi, Reza, and King, Andrew P.
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
The subject of "fairness" in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to address this bias. Most applications to date have been in computer vision, although some work in healthcare has started to emerge. The use of deep learning (DL) in cardiac MR segmentation has led to impressive results in recent years, and such techniques are starting to be translated into clinical practice. However, no work has yet investigated the fairness of such models. In this work, we perform such an analysis for racial/gender groups, focusing on the problem of training data imbalance, using a nnU-Net model trained and evaluated on cine short axis cardiac MR data from the UK Biobank dataset, consisting of 5,903 subjects from 6 different racial groups. We find statistically significant differences in Dice performance between different racial groups. To reduce the racial bias, we investigated three strategies: (1) stratified batch sampling, in which batch sampling is stratified to ensure balance between racial groups; (2) fair meta-learning for segmentation, in which a DL classifier is trained to classify race and jointly optimized with the segmentation model; and (3) protected group models, in which a different segmentation model is trained for each racial group. We also compared the results to the scenario where we have a perfectly balanced database. To assess fairness we used the standard deviation (SD) and skewed error ratio (SER) of the average Dice values. Our results demonstrate that the racial bias results from the use of imbalanced training data, and that all proposed bias mitigation strategies improved fairness, with the best SD and SER resulting from the use of protected group models., Comment: MICCAI 2021 conference
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- 2021
14. Prediction of incident cardiovascular events using machine learning and CMR radiomics
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Pujadas, Esmeralda Ruiz, Raisi-Estabragh, Zahra, Szabo, Liliana, McCracken, Celeste, Morcillo, Cristian Izquierdo, Campello, Víctor M., Martín-Isla, Carlos, Atehortua, Angelica M., Vago, Hajnalka, Merkely, Bela, Maurovich-Horvat, Pal, Harvey, Nicholas C., Neubauer, Stefan, Petersen, Steffen E., and Lekadir, Karim
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- 2023
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15. Exercise capacity following SARS-CoV-2 infection is related to changes in cardiovascular and lung function in military personnel
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Chamley, Rebecca R., Holland, Jennifer L., Collins, Jonathan, Pierce, Kayleigh, Watson, William D., Green, Peregrine G., O'Brien, David, O'Sullivan, Oliver, Barker-Davies, Robert, Ladlow, Peter, Neubauer, Stefan, Bennett, Alexander, Nicol, Edward D., Holdsworth, David A., and Rider, Oliver J.
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- 2024
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16. Etiology and Phenotypes of Cardiomyopathy in Southern Africa: The IMHOTEP Multicenter Pilot Study
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Kraus, Sarah M., Cirota, Jacqui, Pandie, Shahiemah, Thomas, Kandathil, Thomas, Mookenthottathil, Makotoko, Makoali, Damasceno, Albertino, Yiga, Sarah, Greyling, Louwra, Hanekom, Hermanus A., Mateus, Angela, Novela, Celia, Laing, Nakita, September, Unita, Kerbelker, Zita, Suttle, Tessa, Chetwin, Emily, Smit, Francis E., Shaboodien, Gasnat, Chin, Ashley, Sliwa, Karen, Gumedze, Freedom, Neubauer, Stefan, Cooper, Leslie, Watkins, Hugh, and Ntusi, Ntobeko A.B.
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- 2024
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17. Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study
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Ng, Matthew, Guo, Fumin, Biswas, Labonny, Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, and Wright, Graham
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. Methods: We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. Results: We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31--48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75--78% of all images). Conclusion: This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. Significance: Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review., Comment: Accepted to IEEE Transactions on Biomedical Engineering. Copyright (c) 2022 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending an email to pubs-permissions@ieee.org
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- 2020
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18. Myocardial Metabolism in Heart Failure
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Ng, Sher May, Neubauer, Stefan, and Rider, Oliver J
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- 2023
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19. Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank
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Ferdian, Edward, Suinesiaputra, Avan, Fung, Kenneth, Aung, Nay, Lukaschuk, Elena, Barutcu, Ahmet, Maclean, Edd, Paiva, Jose, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E, and Young, Alistair A.
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Physics - Medical Physics - Abstract
Purpose: To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac magnetic resonance tagged images. Methods and Materials: In this retrospective cross-sectional study, 4508 cases from the UK Biobank were split randomly into 3244 training and 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted of 1) a convolutional neural network (CNN) for localization, and 2) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice. Radial and circumferential strain were then calculated from the motion of the landmarks and averaged on a slice basis. Results: Within the test set, myocardial end-systolic circumferential Green strain errors were -0.001 +/- 0.025, -0.001 +/- 0.021, and 0.004 +/- 0.035 in basal, mid, and apical slices respectively (mean +/- std. dev. of differences between predicted and manual strain). The framework reproduced significant reductions in circumferential strain in diabetics, hypertensives, and participants with previous heart attack. Typical processing time was ~260 frames (~13 slices) per second on an NVIDIA Tesla K40 with 12GB RAM, compared with 6-8 minutes per slice for the manual analysis. Conclusions: The fully automated RNNCNN framework for analysis of myocardial strain enabled unbiased strain evaluation in a high-throughput workflow, with similar ability to distinguish impairment due to diabetes, hypertension, and previous heart attack., Comment: accepted in Radiology Cardiothoracic Imaging
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- 2020
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20. Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study
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Raman, Betty, McCracken, Celeste, Cassar, Mark P, Moss, Alastair J, Finnigan, Lucy, Samat, Azlan Helmy A, Ogbole, Godwin, Tunnicliffe, Elizabeth M, Alfaro-Almagro, Fidel, Menke, Ricarda, Xie, Cheng, Gleeson, Fergus, Lukaschuk, Elena, Lamlum, Hanan, McGlynn, Kevin, Popescu, Iulia A, Sanders, Zeena-Britt, Saunders, Laura C, Piechnik, Stefan K, Ferreira, Vanessa M, Nikolaidou, Chrysovalantou, Rahman, Najib M, Ho, Ling-Pei, Harris, Victoria C, Shikotra, Aarti, Singapuri, Amisha, Pfeffer, Paul, Manisty, Charlotte, Kon, Onn M, Beggs, Mark, O'Regan, Declan P, Fuld, Jonathan, Weir-McCall, Jonathan R, Parekh, Dhruv, Steeds, Rick, Poinasamy, Krisnah, Cuthbertson, Dan J, Kemp, Graham J, Semple, Malcolm G, Horsley, Alexander, Miller, Christopher A, O'Brien, Caitlin, Shah, Ajay M, Chiribiri, Amedeo, Leavy, Olivia C, Richardson, Matthew, Elneima, Omer, McAuley, Hamish J C, Sereno, Marco, Saunders, Ruth M, Houchen-Wolloff, Linzy, Greening, Neil J, Bolton, Charlotte E, Brown, Jeremy S, Choudhury, Gourab, Diar Bakerly, Nawar, Easom, Nicholas, Echevarria, Carlos, Marks, Michael, Hurst, John R, Jones, Mark G, Wootton, Daniel G, Chalder, Trudie, Davies, Melanie J, De Soyza, Anthony, Geddes, John R, Greenhalf, William, Howard, Luke S, Jacob, Joseph, Man, William D-C, Openshaw, Peter J M, Porter, Joanna C, Rowland, Matthew J, Scott, Janet T, Singh, Sally J, Thomas, David C, Toshner, Mark, Lewis, Keir E, Heaney, Liam G, Harrison, Ewen M, Kerr, Steven, Docherty, Annemarie B, Lone, Nazir I, Quint, Jennifer, Sheikh, Aziz, Zheng, Bang, Jenkins, R Gisli, Cox, Eleanor, Francis, Susan, Halling-Brown, Mark, Chalmers, James D, Greenwood, John P, Plein, Sven, Hughes, Paul J C, Thompson, A A Roger, Rowland-Jones, Sarah L, Wild, James M, Kelly, Matthew, Treibel, Thomas A, Bandula, Steven, Aul, Raminder, Miller, Karla, Jezzard, Peter, Smith, Stephen, Nichols, Thomas E, McCann, Gerry P, Evans, Rachael A, Wain, Louise V, Brightling, Christopher E, Neubauer, Stefan, Baillie, J K, Shaw, Alison, Hairsine, Brigid, Kurasz, Claire, Henson, Helen, Armstrong, Lisa, Shenton, Liz, Dobson, H, Dell, Amanda, Lucey, Alice, Price, Andrea, Storrie, Andrew, Pennington, Chris, Price, Claire, Mallison, Georgia, Willis, Gemma, Nassa, Heeah, Haworth, Jill, Hoare, Michaela, Hawkings, Nancy, Fairbairn, Sara, Young, Susan, Walker, S, Jarrold, I, Sanderson, Amy, David, C, Chong-James, K, Zongo, O, James, W Y, Martineau, A, King, Bernie, Armour, C, McAulay, D, Major, E, McGinness, Jade, McGarvey, L, Magee, N, Stone, Roisin, Drain, S, Craig, T, Bolger, A, Haggar, Ahmed, Lloyd, Arwel, Subbe, Christian, Menzies, Daniel, Southern, David, McIvor, Emma, Roberts, K, Manley, R, Whitehead, Victoria, Saxon, W, Bularga, A, Mills, N L, El-Taweel, Hosni, Dawson, Joy, Robinson, Leanne, Saralaya, Dinesh, Regan, Karen, Storton, Kim, Brear, Lucy, Amoils, S, Bermperi, Areti, Elmer, Anne, Ribeiro, Carla, Cruz, Isabel, Taylor, Jessica, Worsley, J, Dempsey, K, Watson, L, Jose, Sherly, Marciniak, S, Parkes, M, McQueen, Alison, Oliver, Catherine, Williams, Jenny, Paradowski, Kerry, Broad, Lauren, Knibbs, Lucy, Haynes, Matthew, Sabit, Ramsey, Milligan, L, Sampson, Claire, Hancock, Alyson, Evenden, Cerys, Lynch, Ceri, Hancock, Kia, Roche, Lisa, Rees, Meryl, Stroud, Natalie, Thomas-Woods, T, Heller, S, Robertson, E, Young, B, Wassall, Helen, Babores, M, Holland, Maureen, Keenan, Natalie, Shashaa, Sharlene, Price, Carly, Beranova, Eva, Ramos, Hazel, Weston, Heather, Deery, Joanne, Austin, Liam, Solly, Reanne, Turney, Sharon, Cosier, Tracey, Hazelton, Tracy, Ralser, M, Wilson, Ann, Pearce, Lorraine, Pugmire, S, Stoker, Wendy, McCormick, W, Dewar, A, Arbane, Gill, Kaltsakas, G, Kerslake, Helen, Rossdale, J, Bisnauthsing, Karen, Aguilar Jimenez, Laura A, Martinez, L M, Ostermann, Marlies, Magtoto, Murphy M, Hart, Nicholas, Marino, Philip, Betts, Sarah, Solano, Teresa S, Arias, Ava Maria, Prabhu, A, Reed, Annabel, Wrey Brown, Caroline, Griffin, Denise, Bevan, Emily, Martin, Jane, Owen, J, Alvarez Corral, Maria, Williams, Nick, Payne, Sheila, Storrar, Will, Layton, Alison, Lawson, Cathy, Mills, Clare, Featherstone, James, Stephenson, Lorraine, Burdett, Tracy, Ellis, Y, Richards, A, Wright, C, Sykes, D L, Brindle, K, Drury, Katie, Holdsworth, L, Crooks, M G, Atkin, Paul, Flockton, Rachel, Thackray-Nocera, Susannah, Mohamed, Abdelrahman, Taylor, Abigail, Perkins, Emma, Ross, Gavin, McGuinness, Heather, Tench, Helen, Phipps, Janet, Loosley, Ronda, Wolf-Roberts, Rebecca, Coetzee, S, Omar, Zohra, Ross, Alexandra, Card, Bethany, Carr, Caitlin, King, Clara, Wood, Chloe, Copeland, D, Calvelo, Ellen, Chilvers, Edwin R, Russell, Emily, Gordon, Hussain, Nunag, Jose Lloyd, Schronce, J, March, Katherine, Samuel, Katherine, Burden, L, Evison, Lynsey, McLeavey, Laura, Orriss-Dib, Lorna, Tarusan, Lawrence, Mariveles, Myril, Roy, Maura, Mohamed, Noura, Simpson, Neil, Yasmin, Najira, Cullinan, P, Daly, Patrick, Haq, Sulaimaan, Moriera, Silvia, Fayzan, Tamanah, Munawar, Unber, Nwanguma, Uchechi, Lingford-Hughes, A, Altmann, Danny, Johnston, D, Mitchell, J, Valabhji, J, Price, L, Molyneaux, P L, Thwaites, Ryan S, Walsh, S, Frankel, A, Lightstone, L, Wilkins, M, Willicombe, M, McAdoo, S, Touyz, R, Guerdette, Anne-Marie, Warwick, Katie, Hewitt, Melanie, Reddy, R, White, Sonia, McMahon, A, Hoare, Amy, Knighton, Abigail, Ramos, Albert, Te, Amelie, Jolley, Caroline J, Speranza, Fabio, Assefa-Kebede, Hosanna, Peralta, Ida, Breeze, Jonathon, Shevket, K, Powell, Natassia, Adeyemi, Oluwaseun, Dulawan, Pearl, Adrego, Rita, Byrne, S, Patale, Sheetal, Hayday, A, Malim, M, Pariante, C, Sharpe, C, Whitney, J, Bramham, K, Ismail, K, Wessely, S, Nicholson, T, Ashworth, Andrew, Humphries, Amy, Tan, Ai Lyn, Whittam, Beverley, Coupland, C, Favager, Clair, Peckham, D, Wade, Elaine, Saalmink, Gwen, Clarke, Jude, Glossop, Jodie, Murira, Jennifer, Rangeley, Jade, Woods, Janet, Hall, Lucy, Dalton, Matthhew, Window, Nicola, Beirne, Paul, Hardy, Tim, Coakley, G, Turtle, Lance, Berridge, Anthony, Cross, Andy, Key, Angela L, Rowe, Anna, Allt, Ann Marie, Mears, Chloe, Malein, Flora, Madzamba, Gladys, Hardwick, H E, Earley, Joanne, Hawkes, Jenny, Pratt, James, Wyles, J, Tripp, K A, Hainey, Kera, Allerton, Lisa, Lavelle-Langham, L, Melling, Lucy, Wajero, Lilian O, Poll, L, Noonan, Matthew J, French, N, Lewis-Burke, N, Williams-Howard, S A, Cooper, Shirley, Kaprowska, Sabina, Dobson, S L, Marsh, Sophie, Highett, Victoria, Shaw, V, Beadsworth, M, Defres, S, Watson, Ekaterina, Tiongson, Gerlynn F, Papineni, Padmasayee, Gurram, Sambasivarao, Diwanji, Shalin N, Quaid, Sheena, Briggs, A, Hastie, Claire, Rogers, Natalie, Stensel, D, Bishop, L, McIvor, K, Rivera-Ortega, P, Al-Sheklly, B, Avram, Cristina, Faluyi, David, Blaikely, J, Piper Hanley, K, Radhakrishnan, K, Buch, M, Hanley, N A, Odell, Natasha, Osbourne, Rebecca, Stockdale, Sue, Felton, T, Gorsuch, T, Hussell, T, Kausar, Zunaira, Kabir, T, McAllister-Williams, H, Paddick, S, Burn, D, Ayoub, A, Greenhalgh, Alan, Sayer, A, Young, A, Price, D, Burns, G, MacGowan, G, Fisher, Helen, Tedd, H, Simpson, J, Jiwa, Kasim, Witham, M, Hogarth, Philip, West, Sophie, Wright, S, McMahon, Michael J, Neill, Paula, Dougherty, Andrew, Morrow, A, Anderson, David, Grieve, D, Bayes, Hannah, Fallon, K, Mangion, K, Gilmour, L, Basu, N, Sykes, R, Berry, C, McInnes, I B, Donaldson, A, Sage, E K, Barrett, Fiona, Welsh, B, Bell, Murdina, Quigley, Jackie, Leitch, Karen, Macliver, L, Patel, Manish, Hamil, R, Deans, Andrew, Furniss, J, Clohisey, S, Elliott, Anne, Solstice, A R, Deas, C, Tee, Caroline, Connell, David, Sutherland, Debbie, George, J, Mohammed, S, Bunker, Jenny, Holmes, Katie, Dipper, A, Morley, Anna, Arnold, David, Adamali, H, Welch, H, Morrison, Leigh, Stadon, Louise, Maskell, Nick, Barratt, Shaney, Dunn, Sarah, Waterson, Samuel, Jayaraman, Bhagy, Light, Tessa, Selby, N, Hosseini, A, Shaw, Karen, Almeida, Paula, Needham, Robert, Thomas, Andrew K, Matthews, Laura, Gupta, Ayushman, Nikolaidis, Athanasios, Dupont, Catherine, Bonnington, J, Chrystal, Melanie, Greenhaff, P L, Linford, S, Prosper, Sabrina, Jang, W, Alamoudi, Asma, Bloss, Angela, Megson, Clare, Nicoll, Debby, Fraser, Emily, Pacpaco, Edmund, Conneh, Florence, Ogg, G, McShane, H, Koychev, Ivan, Chen, Jin, Pimm, John, Ainsworth, Mark, Pavlides, M, Sharpe, M, Havinden-Williams, May, Petousi, Nayia, Talbot, Nick, Carter, Penny, Kurupati, Prathiba, Dong, T, Peng, Yanchun, Burns, A, Kanellakis, N, Korszun, A, Connolly, B, Busby, J, Peto, T, Patel, B, Nolan, C M, Cristiano, Daniele, Walsh, J A, Liyanage, Kamal, Gummadi, Mahitha, Dormand, N, Polgar, Oliver, George, P, Barker, R E, Patel, Suhani, Gibbons, M, Matila, Darwin, Jarvis, Hannah, Lim, Lai, Olaosebikan, Olaoluwa, Ahmad, Shanaz, Brill, Simon, Mandal, S, Laing, C, Michael, Alice, Reddy, A, Johnson, C, Baxendale, H, Parfrey, H, Mackie, J, Newman, J, Pack, Jamie, Parmar, J, Paques, K, Garner, Lucie, Harvey, Alice, Summersgill, C, Holgate, D, Hardy, E, Oxton, J, Pendlebury, Jessica, McMorrow, L, Mairs, N, Majeed, N, Dark, P, Ugwuoke, R, Knight, Sean, Whittaker, S, Strong-Sheldrake, Sophia, Matimba-Mupaya, Wadzanai, Chowienczyk, P, Pattenadk, Dibya, Hurditch, E, Chan, Flora, Carborn, H, Foot, H, Bagshaw, J, Hockridge, J, Sidebottom, J, Lee, Ju Hee, Birchall, K, Turner, Kim, Haslam, L, Holt, L, Milner, L, Begum, M, Marshall, M, Steele, N, Tinker, N, Ravencroft, Phillip, Butcher, Robyn, Misra, S, Coburn, Zach, Fairman, Alexandra, Ford, Amber, Holbourn, Ailsa, Howell, Alice, Lawrie, Allan, Lye, Alison, Mbuyisa, Angeline, Zawia, Amira, Holroyd-Hind, B, Thamu, B, Clark, Cameron, Jarman, Claire, Norman, C, Roddis, C, Foote, David, Lee, Elvina, Ilyas, F, Stephens, G, Newell, Helen, Turton, Helena, Macharia, Irene, Wilson, Imogen, Cole, Joby, McNeill, J, Meiring, J, Rodger, J, Watson, James, Chapman, Kerry, Harrington, Kate, Chetham, Luke, Hesselden, L, Nwafor, Lorenza, Dixon, Myles, Plowright, Megan, Wade, Phillip, Gregory, Rebecca, Lenagh, Rebecca, Stimpson, R, Megson, Sharon, Newman, Tom, Cheng, Yutung, Goodwin, Camelia, Heeley, Cheryl, Sissons, D, Sowter, D, Gregory, Heidi, Wynter, Inez, Hutchinson, John, Kirk, Jill, Bennett, Kaytie, Slack, Katie, Allsop, Lynne, Holloway, Leah, Flynn, Margaret, Gill, Mandy, Greatorex, M, Holmes, Megan, Buckley, Phil, Shelton, Sarah, Turner, Sarah, Sewell, Terri Ann, Whitworth, V, Lovegrove, Wayne, Tomlinson, Johanne, Warburton, Louise, Painter, Sharon, Vickers, Carinna, Redwood, Dawn, Tilley, Jo, Palmer, Sue, Wainwright, Tania, Breen, G, Hotopf, M, Dunleavy, A, Teixeira, J, Ali, Mariam, Mencias, Mark, Msimanga, N, Siddique, Sulman, Samakomva, T, Tavoukjian, Vera, Forton, D, Ahmed, R, Cook, Amanda, Thaivalappil, Favas, Connor, Lynda, Rees, Tabitha, McNarry, M, Williams, N, McCormick, Jacqueline, McIntosh, Jerome, Vere, Joanne, Coulding, Martina, Kilroy, Susan, Turner, Victoria, Butt, Al-Tahoor, Savill, Heather, Fraile, Eva, Ugoji, Jacinta, Landers, G, Lota, Harpreet, Portukhay, Sofiya, Nasseri, Mariam, Daniels, Alison, Hormis, Anil, Ingham, Julie, Zeidan, Lisa, Osborne, Lynn, Chablani, Manish, Banerjee, A, David, A, Pakzad, A, Rangelov, B, Williams, B, Denneny, E, Willoughby, J, Xu, M, Mehta, P, Batterham, R, Bell, R, Aslani, S, Lilaonitkul, W, Checkley, A, Bang, Dongchun, Basire, Donna, Lomas, D, Wall, E, Plant, Hannah, Roy, K, Heightman, M, Lipman, M, Merida Morillas, Marta, Ahwireng, Nyarko, Chambers, R C, Jastrub, Roman, Logan, S, Hillman, T, Botkai, A, Casey, A, Neal, A, Newton-Cox, A, Cooper, B, Atkin, C, McGee, C, Welch, C, Wilson, D, Sapey, E, Qureshi, H, Hazeldine, J, Lord, J M, Nyaboko, J, Short, J, Stockley, J, Dasgin, J, Draxlbauer, K, Isaacs, K, Mcgee, K, Yip, K P, Ratcliffe, L, Bates, M, Ventura, M, Ahmad Haider, N, Gautam, N, Baggott, R, Holden, S, Madathil, S, Walder, S, Yasmin, S, Hiwot, T, Jackson, T, Soulsby, T, Kamwa, V, Peterkin, Z, Suleiman, Z, Chaudhuri, N, Wheeler, H, Djukanovic, R, Samuel, R, Sass, T, Wallis, T, Marshall, B, Childs, C, Marouzet, E, Harvey, M, Fletcher, S, Dickens, C, Beckett, P, Nanda, U, Daynes, E, Charalambou, A, Yousuf, A J, Lea, A, Prickett, A, Gooptu, Bibek, Hargadon, Beverley, Bourne, Charlotte, Christie, C, Edwardson, C, Lee, D, Baldry, E, Stringer, E, Woodhead, F, Mills, G, Arnold, H, Aung, H, Qureshi, I N, Finch, J, Skeemer, J, Hadley, K, Khunti, Kamlesh, Carr, Liesel, Ingram, L, Aljaroof, M, Bakali, M, Bakau, M, Baldwin, M, Bourne, Michelle, Pareek, Manish, Soares, M, Tobin, Martin, Armstrong, Natalie, Brunskill, Nigel, Goodman, N, Cairns, P, Haldar, Pranab, McCourt, P, Dowling, R, Russell, Richard, Diver, Sarah, Edwards, Sarah, Glover, Sarah, Parker, S, Siddiqui, Salman, Ward, T J C, Mcnally, T, Thornton, T, Yates, Tom, Ibrahim, W, Monteiro, Will, Thickett, D, Wilkinson, D, Broome, M, McArdle, P, Upthegrove, R, Wraith, D, Langenberg, C, Summers, C, Bullmore, E, Heeney, J L, Schwaeble, W, Sudlow, C L, Adeloye, D, Newby, D E, Rudan, I, Shankar-Hari, M, Thorpe, M, Pius, R, Walmsley, S, McGovern, A, Ballard, C, Allan, L, Dennis, J, Cavanagh, J, Petrie, J, O'Donnell, K, Spears, M, Sattar, N, MacDonald, S, Guthrie, E, Henderson, M, Guillen Guio, Beatriz, Zhao, Bang, Lawson, C, Overton, Charlotte, Taylor, Chris, Tong, C, Mukaetova-Ladinska, Elizabeta, Turner, E, Pearl, John E, Sargant, J, Wormleighton, J, Bingham, Michelle, Sharma, M, Steiner, Mike, Samani, Nilesh, Novotny, Petr, Free, Rob, Allen, R J, Finney, Selina, Terry, Sarah, Brugha, Terry, Plekhanova, Tatiana, McArdle, A, Vinson, B, Spencer, L G, Reynolds, W, Ashworth, M, Deakin, B, Chinoy, H, Abel, K, Harvie, M, Stanel, S, Rostron, A, Coleman, C, Baguley, D, Hufton, E, Khan, F, Hall, I, Stewart, I, Fabbri, L, Wright, L, Kitterick, P, Morriss, R, Johnson, S, Bates, A, Antoniades, C, Clark, D, Bhui, K, Channon, K M, Motohashi, K, Sigfrid, L, Husain, M, Webster, M, Fu, X, Li, X, Kingham, L, Klenerman, P, Miiler, K, Carson, G, Simons, G, Huneke, N, Calder, P C, Baldwin, D, Bain, S, Lasserson, D, Daines, L, Bright, E, Stern, M, Crisp, P, Dharmagunawardena, R, Reddington, A, Wight, A, Bailey, L, Ashish, A, Robinson, E, Cooper, J, Broadley, A, Turnbull, A, Brookes, C, Sarginson, C, Ionita, D, Redfearn, H, Elliott, K, Barman, L, Griffiths, L, Guy, Z, Gill, Rhyan, Nathu, Rashmita, Harris, Edward, Moss, P, Finnigan, J, Saunders, Kathryn, Saunders, Peter, Kon, S, Kon, Samantha S, O'Brien, Linda, Shah, K, Shah, P, Richardson, Emma, Brown, V, Brown, M, Brown, Jo, Brown, J, Brown, Ammani, Brown, Angela, Choudhury, N, Jones, S, Jones, H, Jones, L, Jones, I, Jones, G, Jones, Heather, Jones, Don, Davies, Ffyon, Davies, Ellie, Davies, Kim, Davies, Gareth, Davies, Gwyneth A, Howard, K, Porter, Julie, Rowland, J, Rowland, A, Scott, Kathryn, Singh, Suver, Singh, Claire, Thomas, S, Thomas, Caradog, Lewis, Victoria, Lewis, J, Lewis, D, Harrison, P, Francis, C, Francis, R, Hughes, Rachel Ann, Hughes, Joan, Hughes, A D, Thompson, T, Kelly, S, Smith, D, Smith, Nikki, Smith, Andrew, Smith, Jacqui, Smith, Laurie, Smith, Susan, Evans, Teriann, Evans, Ranuromanana I, Evans, D, Evans, R, Evans, H, and Evans, J
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- 2023
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21. Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS): Assessment & validation of imaging modality performance across the NAFLD spectrum in a prospectively recruited cohort study (the LITMUS imaging study): Study protocol
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Pavlides, Michael, Mózes, Ferenc E., Akhtar, Salma, Wonders, Kristy, Cobbold, Jeremy, Tunnicliffe, Elizabeth M., Allison, Michael, Godfrey, Edmund M., Aithal, Guruprasad P., Francis, Susan, Romero-Gomez, Manuel, Castell, Javier, Fernandez-Lizaranzu, Isabel, Aller, Rocio, González, Rebeca Sigüenza, Agustin, Salvador, Pericàs, Juan M., Boursier, Jerome, Aube, Christophe, Ratziu, Vlad, Wagner, Mathilde, Petta, Salvatore, Antonucci, Michela, Bugianesi, Elisabetta, Faletti, Riccardo, Miele, Luca, Geier, Andreas, Schattenberg, Jörn M., Tilman, Emrich, Ekstedt, Mattias, Lundberg, Peter, Berzigotti, Annalisa, Huber, Adrian T., Papatheodoridis, George, Yki-Järvinen, Hannele, Porthan, Kimmo, Schneider, Moritz Jörg, Hockings, Paul, Shumbayawonda, Elizabeth, Banerjee, Rajarshi, Pepin, Kay, Kalutkiewicz, Mike, Ehman, Richard L., Trylesinksi, Aldo, Coxson, Harvey O., Martic, Miljen, Yunis, Carla, Tuthill, Theresa, Bossuyt, Patrick M., Anstee, Quentin M., Neubauer, Stefan, and Harrison, Stephen
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- 2023
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22. Liver disease is a significant risk factor for cardiovascular outcomes – A UK Biobank study
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Roca-Fernandez, Adriana, Banerjee, Rajarshi, Thomaides-Brears, Helena, Telford, Alison, Sanyal, Arun, Neubauer, Stefan, Nichols, Thomas E., Raman, Betty, McCracken, Celeste, Petersen, Steffen E., Ntusi, Ntobeko AB., Cuthbertson, Daniel J., Lai, Michele, Dennis, Andrea, and Banerjee, Amitava
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- 2023
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23. Reduced Left Atrial Rotational Flow Is Independently Associated With Embolic Brain Infarcts
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Spartera, Marco, Stracquadanio, Antonio, Pessoa-Amorim, Guilherme, Harston, George, Mazzucco, Sara, Young, Victoria, Von Ende, Adam, Hess, Aaron T., Ferreira, Vanessa M., Kennedy, James, Neubauer, Stefan, Casadei, Barbara, and Wijesurendra, Rohan S.
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- 2023
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24. Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis
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Anstee, Quentin M, Daly, Ann K, Govaere, Olivier, Cockell, Simon, Tiniakos, Dina, Bedossa, Pierre, Burt, Alastair, Oakley, Fiona, Cordell, Heather J, Day, Christopher P, Wonders, Kristy, Missier, Paolo, McTeer, Matthew, Vale, Luke, Oluboyede, Yemi, Breckons, Matt, Bossuyt, Patrick M, Zafarmand, Hadi, Vali, Yasaman, Lee, Jenny, Nieuwdorp, Max, Holleboom, Adriaan G, Verheij, Joanne, Ratziu, Vlad, Clément, Karine, Patino-Navarrete, Rafael, Pais, Raluca, Paradis, Valerie, Schuppan, Detlef, Schattenberg, Jörn M, Surabattula, Rambabu, Myneni, Sudha, Straub, Beate K, Vidal-Puig, Toni, Vacca, Michele, Rodrigues-Cuenca, Sergio, Allison, Mike, Kamzolas, Ioannis, Petsalaki, Evangelia, Campbell, Mark, Lelliott, Chris J, Davies, Susan, Orešič, Matej, Hyötyläinen, Tuulia, McGlinchey, Aiden, Mato, Jose M, Millet, Óscar, Dufour, Jean-François, Berzigotti, Annalisa, Masoodi, Mojgan, Pavlides, Michael, Harrison, Stephen, Neubauer, Stefan, Cobbold, Jeremy, Mozes, Ferenc, Akhtar, Salma, Olodo-Atitebi, Seliat, Banerjee, Rajarshi, Kelly, Matt, Shumbayawonda, Elizabeth, Dennis, Andrea, Andersson, Anneli, Wigley, Ioan, Romero-Gómez, Manuel, Gómez-González, Emilio, Ampuero, Javier, Castell, Javier, Gallego-Durán, Rocío, Fernández, Isabel, Montero-Vallejo, Rocío, Karsdal, Morten, Rasmussen, Daniel Guldager Kring, Leeming, Diana Julie, Sinisi, Antonia, Musa, Kishwar, Sandt, Estelle, Tonini, Manuela, Bugianesi, Elisabetta, Rosso, Chiara, Armandi, Angelo, Marra, Fabio, Gastaldelli, Amalia, Svegliati, Gianluca, Boursier, Jérôme, Francque, Sven, Vonghia, Luisa, Driessen, Ann, Ekstedt, Mattias, Kechagias, Stergios, Yki-Järvinen, Hannele, Porthan, Kimmo, Arola, Johanna, van Mil, Saskia, Papatheodoridis, George, Cortez-Pinto, Helena, Rodrigues, Cecilia M P, Valenti, Luca, Pelusi, Serena, Petta, Salvatore, Pennisi, Grazia, Miele, Luca, Geier, Andreas, Trautwein, Christian, Reißing, Johanna, Aithal, Guruprasad P, Francis, Susan, Palaniyappan, Naaventhan, Bradley, Christopher, Hockings, Paul, Schneider, Moritz, Newsome, Philip, Hübscher, Stefan, Wenn, David, Rosenquist, Christian, Trylesinski, Aldo, Mayo, Rebeca, Alonso, Cristina, Duffin, Kevin, Perfield, James W, Chen, Yu, Yunis, Carla, Tuthill, Theresa, Harrington, Magdalena Alicia, Miller, Melissa, Chen, Yan, McLeod, Euan James, Ross, Trenton, Bernardo, Barbara, Schölch, Corinna, Ertle, Judith, Younes, Ramy, Oldenburger, Anouk, Coxson, Harvey, Ostroff, Rachel, Alexander, Leigh, Biegel, Hannah, Kjær, Mette Skalshøi, Harder, Lea Mørch, Davidsen, Peter, Ellegaard, Jens, Balp, Maria-Magdalena, Brass, Clifford, Jennings, Lori, Martic, Miljen, Löffler, Jürgen, Applegate, Douglas, Shankar, Sudha, Torstenson, Richard, Lindén, Daniel, Fournier-Poizat, Céline, Llorca, Anne, Kalutkiewicz, Michael, Pepin, Kay, Ehman, Richard, Horan, Gerald, Ho, Gideon, Tai, Dean, Chng, Elaine, Patterson, Scott D, Billin, Andrew, Doward, Lynda, Twiss, James, Thakker, Paresh, Derdak, Zoltan, Landgren, Henrik, Lackner, Carolin, Gouw, Annette, Hytiroglou, Prodromos, Mózes, Ferenc E, Lee, Jenny A, Alzoubi, Osama, Staufer, Katharina, Trauner, Michael, Paternostro, Rafael, Stauber, Rudolf E, van Dijk, Anne-Marieke, Mak, Anne Linde, de Saint Loup, Marc, Shima, Toshihide, Gaia, Silvia, Shalimar, Lupșor-Platon, Monica, Wong, Vincent Wai-Sun, Li, Guanlin, Wong, Grace Lai-Hung, Karlas, Thomas, Wiegand, Johannes, Sebastiani, Giada, Tsochatzis, Emmanuel, Liguori, Antonio, Yoneda, Masato, Nakajima, Atsushi, Hagström, Hannes, Akbari, Camilla, Hirooka, Masashi, Chan, Wah-Kheong, Mahadeva, Sanjiv, Rajaram, Ruveena, Zheng, Ming-Hua, George, Jacob, Eslam, Mohammed, Viganò, Mauro, Ridolfo, Sofia, Aithal, Guruprasad Padur, Lee, Dae Ho, Nasr, Patrik, Cassinotto, Christophe, de Lédinghen, Victor, Mendoza, Yuly P, Noureddin, Mazen, Truong, Emily, and Harrison, Stephen A
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- 2023
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25. Abstract 18320: Vessel-Specific Coronary Inflammation Quantified Using Perivascular Fat Attenuation Index Score on CCTA Detects the Vulnerable Coronary Artery and Predicts Acute Plaque Events
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Chan, Kenneth, Wahome, Elizabeth, Antonopoulos, Alexios S, Nicol, Edward, Volpe, Lucrezia, West, Henry, Patel, Parijat, Tomlins, Pete, Neubauer, Stefan, Channon, Keith M, and Antoniades, Charalambos
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- 2023
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26. Abstract 16562: Subclinical Flow Inefficiencies in Non-Obstructive Hypertrophic Cardiomyopathy Subgroups Revealed by 4D Flow Cardiovascular Magnetic Resonance
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Pola, Karin, Ashkir, Zakariye, Myerson, Saul G, Arheden, Håkan, Watkins, Hugh, Neubauer, Stefan, Arvidsson, Per M, and Raman, Betty
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- 2023
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27. Abstract 14282: Electrocardiography Rules out Myocardial Abnormalities on Cardiac Magnetic Resonance Imaging in Post-Hospitalized COVID-19 Patients: A Multicenter Observational Study
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Abd Samat, Azlan Helmy, Mahmod, Masliza, Lewandowski, Adam J, Cassar, Mark Philip, Akhtar, Mohammed, Moss, Alastair, Manisty, Charlotte, Treibel, Thomas, Ashkir, Zakariye, McCracken, Celeste, Lukaschuk, Elena, Piechnik, Stefan, Ferreira, Vanessa, Xie, Cheng, Cuthbertson, Dan, Kemp, Graham, Nikolaidou, Chrysovalantou, Miller, Christopher, Chiribiri, Amedeo, OʼRegan, Declan, Francis, Susan, Steeds, Richard P, Weir-McCall, Jonathan, Wild, Jim M, Plein, Sven, McCann, Gerry, Evans, Rachael, brightling, chris, Neubauer, Stefan, and Raman, Betty
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- 2023
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28. Impact of Sleep Duration and Chronotype on Cardiac Structure and Function: The UK Biobank Study
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Khanji, Mohammed Y., Karim, Shahid, Cooper, Jackie, Chahal, Anwar, Aung, Nay, Somers, Virend K., Neubauer, Stefan, and Petersen, Steffen E.
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- 2023
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29. Ethnic differences in the indirect effects of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: a population-based, observational cohort study using the OpenSAFELY platform
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Chaturvedi, Nishi, Park, Chloe, Carnemolla, Alisia, Williams, Dylan, Knueppel, Anika, Boyd, Andy, Turner, Emma L., Evans, Katharine M., Thomas, Richard, Berman, Samantha, McLachlan, Stela, Crane, Matthew, Whitehorn, Rebecca, Oakley, Jacqui, Foster, Diane, Woodward, Hannah, Campbell, Kirsteen C., Timpson, Nicholas, Kwong, Alex, Soares, Ana Goncalves, Griffith, Gareth, Toms, Renin, Jones, Louise, Annie, Herbert, Mitchell, Ruth, Palmer, Tom, Sterne, Jonathan, Walker, Venexia, Huntley, Lizzie, Fox, Laura, Denholm, Rachel, Knight, Rochelle, Northstone, Kate, Kanagaratnam, Arun, Horne, Elsie, Forbes, Harriet, North, Teri, Taylor, Kurt, Arab, Marwa A.L., Walker, Scott, Coronado, Jose I.C., Karthikeyan, Arun S., Ploubidis, George, Moltrecht, Bettina, Booth, Charlotte, Parsons, Sam, Wielgoszewska, Bozena, Bridger-Staatz, Charis, Steves, Claire, Thompson, Ellen, Garcia, Paz, Cheetham, Nathan, Bowyer, Ruth, Freydin, Maxim, Roberts, Amy, Goldacre, Ben, Walker, Alex, Morley, Jess, Hulme, William, Nab, Linda, Fisher, Louis, MacKenna, Brian, Andrews, Colm, Curtis, Helen, Hopcroft, Lisa, Green, Amelia, Patalay, Praveetha, Maddock, Jane, Patel, Kishan, Stafford, Jean, Jacques, Wels, Tilling, Kate, Macleod, John, McElroy, Eoin, Shah, Anoop, Silverwood, Richard, Denaxas, Spiros, Flaig, Robin, McCartney, Daniel, Campbell, Archie, Tomlinson, Laurie, Tazare, John, Zheng, Bang, Smeeth, Liam, Herrett, Emily, Cowling, Thomas, Mansfield, Kate, Costello, Ruth E., Wang, Kevin, Mansfield, Kathryn, Mahalingasivam, Viyaasan, Douglas, Ian, Langan, Sinead, Brophy, Sinead, Parker, Michael, Kennedy, Jonathan, McEachan, Rosie, Wright, John, Willan, Kathryn, Badrick, Ellena, Santorelli, Gillian, Yang, Tiffany, Hou, Bo, Steptoe, Andrew, Giorgio, Di Gessa, Zhu, Jingmin, Zaninotto, Paola, Wood, Angela, Cezard, Genevieve, Ip, Samantha, Bolton, Tom, Sampri, Alexia, Rafeti, Elena, Almaghrabi, Fatima, Sheikh, Aziz, Shah, Syed A., Katikireddi, Vittal, Shaw, Richard, Hamilton, Olivia, Green, Michael, Kromydas, Theocharis, Kopasker, Daniel, Greaves, Felix, Willans, Robert, Glen, Fiona, Sharp, Steve, Hughes, Alun, Wong, Andrew, Howes, Lee Hamill, Rapala, Alicja, Nigrelli, Lidia, McArdle, Fintan, Beckford, Chelsea, Raman, Betty, Dobson, Richard, Folarin, Amos, Stewart, Callum, Ranjan, Yatharth, Carpentieri, Jd, Sheard, Laura, Fang, Chao, Baz, Sarah, Gibson, Andy, Kellas, John, Neubauer, Stefan, Piechnik, Stefan, Lukaschuk, Elena, Saunders, Laura C., Wild, James M., Smith, Stephen, Jezzard, Peter, Tunnicliffe, Elizabeth, Sanders, Zeena-Britt, Finnigan, Lucy, Ferreira, Vanessa, Green, Mark, Rhead, Rebecca, Kibble, Milla, Wei, Yinghui, Lemanska, Agnieszka, Perez-Reche, Francisco, Piehlmaier, Dominik, Teece, Lucy, Parker, Edward, Walker, Alex J., Inglesby, Peter, Curtis, Helen J., Morton, Caroline E., Morley, Jessica, Mehrkar, Amir, Bacon, Sebastian C.J., Hickman, George, Croker, Richard, Evans, David, Ward, Tom, DeVito, Nicholas J., Green, Amelia C.A., Massey, Jon, Smith, Rebecca M., Hulme, William J., Davy, Simon, Andrews, Colm D., Hopcroft, Lisa E.M., Drysdale, Henry, Dillingham, Iain, Park, Robin Y., Higgins, Rose, Cunningham, Christine, Wiedemann, Milan, Maude, Steven, Macdonald, Orla, Butler-Cole, Ben F.C., O'Dwyer, Thomas, Stables, Catherine L., Wood, Christopher, Brown, Andrew D., Speed, Victoria, Bridges, Lucy, Schaffer, Andrea L., Walters, Caroline E., Rentsch, Christopher T., Bhaskaran, Krishnan, Schultze, Anna, Williamson, Elizabeth J., McDonald, Helen I., Tomlinson, Laurie A., Mathur, Rohini, Eggo, Rosalind M., Wing, Kevin, Wong, Angel Y.S., Grieve, Richard, Grint, Daniel J., Mansfield, Kathryn E., Douglas, Ian J., Evans, Stephen J.W., Walker, Jemma L., Cowling, Thomas E., Herrett, Emily L., Parker, Edward P.K., Bates, Christopher, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, O'Hanlon, Shaun, Eavis, Alex, Jarvis, Richard, Avramov, Dima, Griffiths, Paul, Fowles, Aaron, Parkes, Nasreen, Nicholson, Brian, Perera, Rafael, Harrison, David, Khunti, Kamlesh, Sterne, Jonathan AC., Quint, Jennifer, Henderson, Alasdair D., Carreira, Helena, Bidulka, Patrick, Warren-Gash, Charlotte, Hayes, Joseph F., Quint, Jennifer K., Katikireddi, Srinivasa Vittal, and Langan, Sinéad M.
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- 2023
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30. Ischemic Heart Disease and Vascular Risk Factors Are Associated With Accelerated Brain Aging
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Rauseo, Elisa, Salih, Ahmed, Raisi-Estabragh, Zahra, Aung, Nay, Khanderia, Neha, Slabaugh, Gregory G., Marshall, Charles R., Neubauer, Stefan, Radeva, Petia, Galazzo, Ilaria Boscolo, Menegaz, Gloria, and Petersen, Steffen E.
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- 2023
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31. Machine learning evaluation of LV outflow obstruction in hypertrophic cardiomyopathy using three-chamber cardiovascular magnetic resonance
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Sahota, Manisha, Saraskani, Sepas Ryan, Xu, Hao, Li, Liandong, Majeed, Abdul Wahab, Hermida, Uxio, Neubauer, Stefan, Desai, Milind, Weintraub, William, Desvigne-Nickens, Patrice, Schulz-Menger, Jeanette, Kwong, Raymond Y., Kramer, Christopher M., Young, Alistair A., and Lamata, Pablo
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- 2022
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32. Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
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Qin, Chen, Bai, Wenjia, Schlemper, Jo, Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, and Rueckert, Daniel
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicting cardiac motion estimation and segmentation directly from undersampled data, which are two important steps in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In particular, a unified model consisting of both motion estimation branch and segmentation branch is learned by optimising the two tasks simultaneously. Additional corresponding fully-sampled images are incorporated into the network as a parallel sub-network to enhance and guide the learning during the training process. Experimental results using cardiac MR images from 220 subjects show that the proposed model is robust to undersampled data and is capable of predicting results that are close to that from fully-sampled ones, while bypassing the usual image reconstruction stage., Comment: This work is published at MLMIR 2018: Machine Learning for Medical Image Reconstruction
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- 2019
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33. 3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata
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Attar, Rahman, Pereanez, Marco, Bowles, Christopher, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., and Frangi, Alejandro F.
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters. The proposed method uses the promising ability of statistical shape models to simplify shape complexity and variability together with the advantages of convolutional neural networks for the extraction of solid visual features. To the best of our knowledge, this is the first work that uses such an approach for 3D cardiac shape prediction. We validated our proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles. Our results show broadly significant agreement with the reference shapes in terms of the estimated volume of the cardiac ventricles, myocardial mass, 3D Dice, and mean and Hausdorff distance., Comment: Accepted for publication in MICCAI 2019
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- 2019
34. Improving the generalizability of convolutional neural network-based segmentation on CMR images
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Chen, Chen, Bai, Wenjia, Davies, Rhodri H., Bhuva, Anish N., Manisty, Charlotte, Moon, James C., Aung, Nay, Lee, Aaron M., Sanghvi, Mihir M., Fung, Kenneth, Paiva, Jose Miguel, Petersen, Steffen E., Lukaschuk, Elena, Piechnik, Stefan K., Neubauer, Stefan, and Rueckert, Daniel
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task., Comment: 15 pages, 8 figures
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- 2019
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35. Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
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Robinson, Robert, Valindria, Vanya V., Bai, Wenjia, Oktay, Ozan, Kainz, Bernhard, Suzuki, Hideaki, Sanghvi, Mihir M., Aung, Nay, Paiva, Jos$é$ Miguel, Zemrak, Filip, Fung, Kenneth, Lukaschuk, Elena, Lee, Aaron M., Carapella, Valentina, Kim, Young Jin, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Page, Chris, Matthews, Paul M., Rueckert, Daniel, and Glocker, Ben
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study., Comment: 14 pages, 7 figures, Journal of Cardiovascular Magnetic Resonance
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- 2019
36. High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort
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Attar, Rahman, Pereanez, Marco, Gooya, Ali, Alba, Xenia, Zhang, Le, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., and Frangi, Alejandro F.
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the cardiac structures, assessment of the segmentation quality, and parsing of cardiac functional indexes. We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is capable of performing end-to-end image processing including: data organisation, image quality assessment, shape model initialisation, segmentation, segmentation quality assessment, and functional parameter computation; all without any user interaction. To the best of our knowledge,this is the first paper tackling the fully automatic 3D analysis of the UKB population study, providing reference ranges for all key cardiovascular functional indexes, from both left and right ventricles of the heart. We tested our workflow on a reference cohort of 800 healthy subjects for which manual delineations, and reference functional indexes exist. Our results show statistically significant agreement between the manually obtained reference indexes, and those automatically computed using our framework., Comment: Accepted in STACOM workshop of MICCAI2018
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- 2019
37. Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction
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West, Henry W., Siddique, Muhammad, Williams, Michelle C., Volpe, Lucrezia, Desai, Ria, Lyasheva, Maria, Thomas, Sheena, Dangas, Katerina, Kotanidis, Christos P., Tomlins, Pete, Mahon, Ciara, Kardos, Attila, Adlam, David, Graby, John, Rodrigues, Jonathan C.L., Shirodaria, Cheerag, Deanfield, John, Mehta, Nehal N., Neubauer, Stefan, Channon, Keith M., Desai, Milind Y., Nicol, Edward D., Newby, David E., and Antoniades, Charalambos
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- 2023
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38. Efficacy and tolerability of an endogenous metabolic modulator (AXA1125) in fatigue-predominant long COVID: a single-centre, double-blind, randomised controlled phase 2a pilot study
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Finnigan, Lucy E.M., Cassar, Mark Philip, Koziel, Margaret James, Pradines, Joel, Lamlum, Hanan, Azer, Karim, Kirby, Dan, Montgomery, Hugh, Neubauer, Stefan, Valkovič, Ladislav, and Raman, Betty
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- 2023
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39. Myocardial Injury on CMR in Patients With COVID-19 and Suspected Cardiac Involvement
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Vidula, Mahesh K., Rajewska-Tabor, Justyna, Cao, J. Jane, Kang, Yu, Craft, Jason, Mei, Winifred, Chandrasekaran, Preethi S., Clark, Daniel E., Poenar, Ana-Maria, Gorecka, Miroslawa, Malahfji, Maan, Cowan, Eilidh, Kwan, Jennifer M., Reinhardt, Samuel W., Al-Tabatabaee, Sarah, Doeblin, Patrick, Villa, Adriana D.M., Karagodin, Ilya, Alvi, Nazia, Christia, Panagiota, Spetko, Nicholas, Cassar, Mark Philip, Park, Christine, Nambiar, Lakshmi, Turgut, Alper, Azad, Mahan Roosta, Lambers, Moritz, Wong, Timothy C., Salerno, Michael, Kim, Jiwon, Elliott, Michael, Raman, Betty, Neubauer, Stefan, Tsao, Connie W., LaRocca, Gina, Patel, Amit R., Chiribiri, Amedeo, Kelle, Sebastian, Baldassarre, Lauren A., Shah, Dipan J., Hughes, Sean G., Tong, Matthew S., Pyda, Malgorzata, Simonetti, Orlando P., Plein, Sven, and Han, Yuchi
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- 2023
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40. 3-Dimensional Strain Analysis of Hypertrophic Cardiomyopathy: Insights From the NHLBI International HCM Registry
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Heydari, Bobak, Satriano, Alessandro, Jerosch-Herold, Michael, Kolm, Paul, Kim, Dong-Yun, Cheng, Kathleen, Choi, Yuna L., Antiochos, Panagiotis, White, James A., Mahmod, Masliza, Chan, Kenneth, Raman, Betty, Desai, Milind Y., Ho, Carolyn Y., Dolman, Sarahfaye F., Desvigne-Nickens, Patrice, Maron, Martin S., Friedrich, Matthias G., Schulz-Menger, Jeanette, Piechnik, Stefan K., Appelbaum, Evan, Weintraub, William S., Neubauer, Stefan, Kramer, Christopher M., and Kwong, Raymond Y.
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- 2023
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41. Incident Clinical and Mortality Associations of Myocardial Native T1 in the UK Biobank
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Raisi-Estabragh, Zahra, McCracken, Celeste, Hann, Evan, Condurache, Dorina-Gabriela, Harvey, Nicholas C., Munroe, Patricia B., Ferreira, Vanessa M., Neubauer, Stefan, Piechnik, Stefan K., and Petersen, Steffen E.
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- 2023
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42. The cardiac sympathetic co-transmitter neuropeptide Y is pro-arrhythmic following ST-elevation myocardial infarction despite beta-blockade
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Kalla, Manish, Hao, Guoliang, Tapoulal, Nidi, Tomek, Jakub, Liu, Kun, Woodward, Lavinia, Dall’Armellina, Erica, Banning, Adrian P, Choudhury, Robin P, Neubauer, Stefan, Kharbanda, Rajesh K, Channon, Keith M, Ajijola, Olujimi A, Shivkumar, Kalyanam, Paterson, David J, and Herring, Neil
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Medical Physiology ,Biomedical and Clinical Sciences ,Heart Disease ,Neurosciences ,Heart Disease - Coronary Heart Disease ,Cardiovascular ,Prevention ,Animals ,Heart ,Humans ,Neuropeptide Y ,Percutaneous Coronary Intervention ,Rats ,ST Elevation Myocardial Infarction ,Ventricular Fibrillation ,Myocardial infarction ,Percutaneous coronary intervention ,Ventricular tachycardia ,Ventricular fibrillation ,‘Oxford Acute Myocardial Infarction (OxAMI) Study’ ,Cardiorespiratory Medicine and Haematology ,Clinical Sciences ,Cardiovascular System & Hematology ,Cardiovascular medicine and haematology ,Clinical sciences - Abstract
AimsST-elevation myocardial infarction is associated with high levels of cardiac sympathetic drive and release of the co-transmitter neuropeptide Y (NPY). We hypothesized that despite beta-blockade, NPY promotes arrhythmogenesis via ventricular myocyte receptors.Methods and resultsIn 78 patients treated with primary percutaneous coronary intervention, sustained ventricular tachycardia (VT) or fibrillation (VF) occurred in 6 (7.7%) within 48 h. These patients had significantly (P
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- 2020
43. Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN
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Zhang, Le, Gooya, Ali, Pereanez, Marco, Dong, Bo, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., and Frangi, Alejandro F.
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (FD3D) convolutional neural networks (CNNs). In contrast to our previous method employing 2D CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features and enhances the discriminative capacity of the baseline 2D CNN learning framework, thus achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the FD3D CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3D CNN strategy, the proposed FD3D CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5,000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9\%/4.6\%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data., Comment: 12 pages, 5 figures, accepted by IEEE Transactions on Biomedical Engineering
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- 2018
44. Neutrophils incite and macrophages avert electrical storm after myocardial infarction
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Grune, Jana, Lewis, Andrew J. M., Yamazoe, Masahiro, Hulsmans, Maarten, Rohde, David, Xiao, Ling, Zhang, Shuang, Ott, Christiane, Calcagno, David M., Zhou, Yirong, Timm, Kerstin, Shanmuganathan, Mayooran, Pulous, Fadi E., Schloss, Maximillian J., Foy, Brody H., Capen, Diane, Vinegoni, Claudio, Wojtkiewicz, Gregory R., Iwamoto, Yoshiko, Grune, Tilman, Brown, Dennis, Higgins, John, Ferreira, Vanessa M., Herring, Neil, Channon, Keith M., Neubauer, Stefan, Sosnovik, David E., Milan, David J., Swirski, Filip K., King, Kevin R., Aguirre, Aaron D., Ellinor, Patrick T., and Nahrendorf, Matthias
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- 2022
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45. Retained Metabolic Flexibility of the Failing Human Heart
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Watson, William D., Green, Peregrine G., Lewis, Andrew J.M., Arvidsson, Per, De Maria, Giovanni Luigi, Arheden, Håkan, Heiberg, Einar, Clarke, William T., Rodgers, Christopher T., Valkovič, Ladislav, Neubauer, Stefan, Herring, Neil, and Rider, Oliver J.
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- 2023
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46. OS-020 MRI-serum based score accurately identifies liver transplant patients without rejection avoiding need for liver biopsy: a multisite european study
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Schaapman, Jelte, primary, Shumbayawonda, Elizabeth, additional, Castelo-Branco, Miguel, additional, Alves, Filipe Caseiro, additional, Costa, Tania, additional, Fitzpatrick, Emer, additional, Tupper, Katie, additional, Dhawan, Anil, additional, Deheragoda, Maesha, additional, Sticova, Eva, additional, French, Marika, additional, Beyer, Cayden, additional, Rymell, Soubera, additional, Tonev, Dimitar, additional, Verspaget, Hein W, additional, Neubauer, Stefan, additional, Banerjee, Rajarshi, additional, Lamb, Hildo, additional, and Coenraad, Minneke, additional
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- 2024
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47. Long COVID and cardiovascular disease: a prospective cohort study
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Lawson, Claire Alexandra, primary, Moss, Alastair James, additional, Arnold, Jayanth Ranjit, additional, Bagot, Catherine, additional, Banerjee, Amitava, additional, Berry, Colin, additional, Greenwood, John, additional, Hughes, Alun D, additional, Khunti, Kamlesh, additional, Mills, Nicholas L, additional, Neubauer, Stefan, additional, Raman, Betty, additional, Sattar, Naveed, additional, Leavy, Olivia C, additional, Richardson, Matthew, additional, Elneima, Omer, additional, McAuley, Hamish JC, additional, Shikotra, Aarti, additional, Singapuri, Amisha, additional, Sereno, Marco, additional, Saunders, Ruth, additional, Harris, Victoria, additional, Houchen-Wolloff, Linzy, additional, Greening, Neil J, additional, Harrison, Ewen, additional, Docherty, Annemarie B, additional, Lone, Nazir I, additional, Quint, Jennifer Kathleen, additional, Chalmers, James, additional, Ho, Ling-Pei, additional, Horsley, Alex, additional, Marks, Michael, additional, Poinasamy, Krisnah, additional, Evans, Rachael, additional, Wain, Louise V, additional, Brightling, Chris, additional, and McCann, Gerry P, additional
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- 2024
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48. Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study
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Chan, Kenneth, primary, Wahome, Elizabeth, additional, Tsiachristas, Apostolos, additional, Antonopoulos, Alexios S, additional, Patel, Parijat, additional, Lyasheva, Maria, additional, Kingham, Lucy, additional, West, Henry, additional, Oikonomou, Evangelos K, additional, Volpe, Lucrezia, additional, Mavrogiannis, Michail C, additional, Nicol, Edward, additional, Mittal, Tarun K, additional, Halborg, Thomas, additional, Kotronias, Rafail A, additional, Adlam, David, additional, Modi, Bhavik, additional, Rodrigues, Jonathan, additional, Screaton, Nicholas, additional, Kardos, Attila, additional, Greenwood, John P, additional, Sabharwal, Nikant, additional, De Maria, Giovanni Luigi, additional, Munir, Shahzad, additional, McAlindon, Elisa, additional, Sohan, Yogesh, additional, Tomlins, Pete, additional, Siddique, Muhammad, additional, Kelion, Andrew, additional, Shirodaria, Cheerag, additional, Pugliese, Francesca, additional, Petersen, Steffen E, additional, Blankstein, Ron, additional, Desai, Milind, additional, Gersh, Bernard J, additional, Achenbach, Stephan, additional, Libby, Peter, additional, Neubauer, Stefan, additional, Channon, Keith M, additional, Deanfield, John, additional, Antoniades, Charalambos, additional, Thomas, Sheena, additional, Denton, Jon, additional, Farral, Robyn, additional, Taylor, Carolyn, additional, Qin, Wendy, additional, Kasongo, Mary, additional, Anthony, Susan, additional, Banning, Adrian, additional, Xie, Cheng, additional, Kharbanda, Rajesh K, additional, Pritchard, Amy, additional, Syed, Nigar, additional, Fry, Sam, additional, Mathers, Chris, additional, Rose, Anne, additional, Hudson, George, additional, Bajaj, Amrita, additional, Das, Intrajeet, additional, Deshpande, Aparna, additional, Rao, Praveen, additional, Lawday, Dan, additional, Mirsadraee, Saeed, additional, Hudson, Benjamin, additional, Berry, Colin, additional, Marwan, Mohamed, additional, Maurovich-Horvat, Pál, additional, He, Guo-Wei, additional, Lin, Wen-Hua, additional, Fan, Li-Juan, additional, Takahashi, Naohiko, additional, Kondo, Hidekazu, additional, Dai, Neng, additional, Ge, Junbo, additional, Koo, Bon-Kwon, additional, Guglielmo, Marco, additional, Pontone, Gianluca, additional, Huck, Daniel, additional, Benedek, Theodora, additional, Rajani, Ronak, additional, Vilic, Dijana, additional, Aljazzaf, Haleema, additional, Mun, Mak S, additional, Benedetti, Giulia, additional, Preston, Rebecca L, additional, Raisi-Estabragh, Zahra, additional, Connolly, Derek L, additional, Sharma, Vinoda, additional, Grenfell, Rebecca, additional, Bradlow, William, additional, Schmitt, Matthias, additional, Serfaty, Fabiano, additional, Gottlieb, Ilan, additional, Neves, Mario FT, additional, Newby, David E, additional, Dweck, Marc R, additional, Hatem, Stéphane, additional, Redheuil, Alban, additional, Benetos, Georgios, additional, Beer, Meinrad, additional, Granillo, Gastón AR, additional, Selvanayagam, Joseph, additional, Lopez-Jimenez, Francisco, additional, De Bosscher, Ruben, additional, Tavildari, Alain, additional, Figtree, Gemma, additional, Danad, Ibrahim, additional, Shantouf, Ronney, additional, Kietselaer, Bas, additional, Tousoulis, Dimitris, additional, Dangas, George, additional, Mehta, Nehal N, additional, Kontanidis, Christos, additional, Kunadian, Vijay, additional, and Fairbairn, Timothy A, additional
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- 2024
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49. Concurrent Left Ventricular Myocardial Diffuse Fibrosis and Left Atrial Dysfunction Strongly Predict Incident Heart Failure
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Wong, Mark Y.Z., primary, Vargas, Jose D., additional, Naderi, Hafiz, additional, Sanghvi, Mihir M., additional, Raisi-Estabragh, Zahra, additional, Suinesiaputra, Avan, additional, Bonazzola, Rodrigo, additional, Attar, Rahman, additional, Ravikumar, Nishant, additional, Hann, Evan, additional, Neubauer, Stefan, additional, Piechnik, Stefan K., additional, Frangi, Alejandro F., additional, Petersen, Steffen E., additional, and Aung, Nay, additional
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- 2024
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50. Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study
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Siontis, Konstantinos C, primary, Wieczorek, Mikolaj A, additional, Maanja, Maren, additional, Hodge, David O, additional, Kim, Hyung-Kwan, additional, Lee, Hyun-Jung, additional, Lee, Heesun, additional, Lim, Jaehyun, additional, Park, Chan Soon, additional, Ariga, Rina, additional, Raman, Betty, additional, Mahmod, Masliza, additional, Watkins, Hugh, additional, Neubauer, Stefan, additional, Windecker, Stephan, additional, Siontis, George C M, additional, Gersh, Bernard J, additional, Ackerman, Michael J, additional, Attia, Zachi I, additional, Friedman, Paul A, additional, and Noseworthy, Peter A, additional
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- 2024
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