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27 results on '"Mah, Yee"'

1. Framework to generate perfusion map from CT and CTA images in patients with acute ischemic stroke: A longitudinal and cross-sectional study

Author

Tangwiriyasakul, Chayanin, Borges, Pedro, Moriconi, Stefano, Wright, Paul, Mah, Yee-Haur, Teo, James, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge

Subjects
Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Stroke is a leading cause of disability and death. Effective treatment decisions require early and informative vascular imaging. 4D perfusion imaging is ideal but rarely available within the first hour after stroke, whereas plain CT and CTA usually are. Hence, we propose a framework to extract a predicted perfusion map (PPM) derived from CT and CTA images. In all eighteen patients, we found significantly high spatial similarity (with average Spearman's correlation = 0.7893) between our predicted perfusion map (PPM) and the T-max map derived from 4D-CTP. Voxelwise correlations between the PPM and National Institutes of Health Stroke Scale (NIHSS) subscores for L/R hand motor, gaze, and language on a large cohort of 2,110 subjects reliably mapped symptoms to expected infarct locations. Therefore our PPM could serve as an alternative for 4D perfusion imaging, if the latter is unavailable, to investigate blood perfusion in the first hours after hospital admission., Comment: Accepted and presented in SWITCH2023: Stroke Workshop on Imaging and Treatment CHallenges (MICCAI 2023, Vancouver Canada)

Published
2024

2. Unsupervised 3D out-of-distribution detection with latent diffusion models

Author

Graham, Mark S., Pinaya, Walter Hugo Lopez, Wright, Paul, Tudosiu, Petru-Daniel, Mah, Yee H., Teo, James T., Jäger, H. Rolf, Werring, David, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilistic models (DDPMs) have been recently proposed as a robust way to perform reconstruction-based OOD detection on 2D datasets, but do not trivially scale to 3D data. In this work, we propose to use Latent Diffusion Models (LDMs), which enable the scaling of DDPMs to high-resolution 3D medical data. We validate the proposed approach on near- and far-OOD datasets and compare it to a recently proposed, 3D-enabled approach using Latent Transformer Models (LTMs). Not only does the proposed LDM-based approach achieve statistically significant better performance, it also shows less sensitivity to the underlying latent representation, more favourable memory scaling, and produces better spatial anomaly maps. Code is available at https://github.com/marksgraham/ddpm-ood, Comment: Accepted at MICCAI 2023

Published
2023

3. Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models

Author

Pinaya, Walter H. L., Graham, Mark S., Gray, Robert, Da Costa, Pedro F, Tudosiu, Petru-Daniel, Wright, Paul, Mah, Yee H., MacKinnon, Andrew D., Teo, James T., Jager, Rolf, Werring, David, Rees, Geraint, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods
Abstract

Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for anomaly detection in medical imaging. Nonetheless, these models still have some intrinsic weaknesses, such as requiring images to be modelled as 1D sequences, the accumulation of errors during the sampling process, and the significant inference times associated with transformers. Denoising diffusion probabilistic models are a class of non-autoregressive generative models recently shown to produce excellent samples in computer vision (surpassing Generative Adversarial Networks), and to achieve log-likelihoods that are competitive with transformers while having fast inference times. Diffusion models can be applied to the latent representations learnt by autoencoders, making them easily scalable and great candidates for application to high dimensional data, such as medical images. Here, we propose a method based on diffusion models to detect and segment anomalies in brain imaging. By training the models on healthy data and then exploring its diffusion and reverse steps across its Markov chain, we can identify anomalous areas in the latent space and hence identify anomalies in the pixel space. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data involving synthetic and real pathological lesions with much reduced inference times, making their usage clinically viable.

Published
2022

4. Transformer-based out-of-distribution detection for clinically safe segmentation

Author

Graham, Mark S, Tudosiu, Petru-Daniel, Wright, Paul, Pinaya, Walter Hugo Lopez, Jean-Marie, U, Mah, Yee, Teo, James, Jäger, Rolf H, Werring, David, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M Jorge

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular approach to safe processing is to train networks that can provide a measure of their uncertainty, but these tend to fail for inputs that are far outside the training data distribution. Recently, generative modelling approaches have been proposed as an alternative; these can quantify the likelihood of a data sample explicitly, filtering out any out-of-distribution (OOD) samples before further processing is performed. In this work, we focus on image segmentation and evaluate several approaches to network uncertainty in the far-OOD and near-OOD cases for the task of segmenting haemorrhages in head CTs. We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD. We propose performing full 3D OOD detection using a VQ-GAN to provide a compressed latent representation of the image and a transformer to estimate the data likelihood. Our approach successfully identifies images in both the far- and near-OOD cases. We find a strong relationship between image likelihood and the quality of a model's segmentation, making this approach viable for filtering images unsuitable for segmentation. To our knowledge, this is the first time transformers have been applied to perform OOD detection on 3D image data. Code is available at github.com/marksgraham/transformer-ood., Comment: Accepted at MIDL 2022 (Oral)

Published
2022

5. Optimal timing of anticoagulation after acute ischaemic stroke with atrial fibrillation (OPTIMAS): a multicentre, blinded-endpoint, phase 4, randomised controlled trial

Author

Jelley, Benjamin, Hughes, Tom, Evans, Mim, Esteban, Diego Garcia, Knibbs, Lucy, Broad, Lauren, Price, Rebecca, Griebel, Liz Hamer, Hewson, Sian, Thavanesan, Kamy, Mallon, Louise, Smith, Anna, White, Miranda, Zhang, Liqun, Clarke, Brian, Abousleiman, Youssif, Binnie, Lauren, Sim, Cai Hua, Castanheira, Margarida, Humphries, Fiona, Obarey, Sabaa, Feerick, Shez, Lee, Yee Chin, Lewis, Alex, Muhammad, Riham, Francia, Nina, Atang, Ndifreke, Banaras, Azra, Marinescu, Marilena, Ferdinand, Philip, Varquez, Resti, Ponce, Ida, Saxena, Surabhi, O'Brien, Eoin, Reyes, Juliana Delos, Mitchell-Douglas, Jennifer, Francis, Jobbin, Banerjee, Soma, Dave, Vaishali, Mashate, Sheila, Patel, Tulsi, Sekaran, Lakshmanan, Murad, Wahid, Asaipillai, Asokanathan, Sakthivel, Sethuraman, Tate, Margaret, Angus, Jane, Reid, Lisa, Fornolles, Caroline, Sundayi, Saul, Poolon, Lincy, Justin, Francis, Hunte, Sophy, Bhandari, Mohit, Kho, Jules, Cvoro, Vera, Parakramawansha, Ruwan, Couser, Mandy, Hughes, Hannah, Naqvi, Aaizza, Harkness, Kirsty, Richards, Emma, Howe, Jo, Kamara, Chris, Gardner, Jon, Bains, Harjit, Teal, Rachel, Joseph, Jeethu, Benjamin, Jithen, Al-Hussayni, Samer, Thomas, George, Robinson, Faye, Dixon, Lynn, Krishnan, Manju, Slade, Peter, Anjum, Tal, Storton, Sharon, Adie, Katja, Northcott, Keren, Morgan, Katie, Williams, Emilie, Chanashekar, Harinath, Maguire, Holly, Gabriel, Claire, Maren, Deborah, David, Hannah, Clarke, Sheron, Nagaratnam, Kiruba, Nelatur, Varun, Mannava, Neelima, Blasco, Lara, Devine, Joseph, Bathula, Rajaram, Gopi, Parvathy, Mehta, Niharika, Sreedevi Raj, Sreena, Teo, James, Sztriha, Laszio, Mah, Yee, Ankolekar, Sandeep, Sari, Beatrix, Tibajai, Maria, Morgan, Alicia, Recaman, Maria, Bayhonan, Samantha, Belo, Caroline, Finch, Sharon, Keenan, Samantha, Bowring, Angie, Shetty, Ashit, Chan, Siang, Gray, Lucy, Harrison, Thomas, Spooner, Oliver, Kinsella-Perks, Edward, Erumere, Esther, Sanders, Brittany, Sims, Don, Willmot, Mark, Littleton, Edward, Spruce, Elaine, Moody, Lisa, Sheriden, Christopher, Luxmore-Brown, Scott, Neal, Aoife, Beddows, Sophie, Tuna, Maria Assuncao, Misra, Amulya, Penn, Ruth, Mariampillai, Sonia, Anwar, Ijaz, Annamalai, Arunkumar, Whitehouse, Sarah, Shepherd, Lorna, Siddle, Elaine, Chatterjee, Kausik, Leason, Sandra, Davies, Angela, Marigold, Richard James, Frank, Sarah, Baird, Alix, Hannam-Penfold, Tomas, Inacio, Liliana, Smith, Simon, Eveson, David, Musarrat, Kashif, Khan, Shagufta, Harris, Tracy, Chowdhury, Muhibbur, Alam, Sajid, Jamieson, Elena, Anyankpele, Ebitare, Al Shalchi, Farah, Rivers, Vanessa, Bell, Stephanie, Francis, Rebecca, Beeby, Deborah, Finch, Jenny, Macleod, Mary Joan, Guzman-Gutierrez, German, Carter, Karla, Irvine, Janice, Gbadamoshi, Lukuman, Costa, Telma, Heirons, Sarah, Stoney, Hayley, Shaw, Louise, Choulerton, James, Catibog, Darwin, Sattar, Naweed, Myint, Min, Smith, Andy, Serac, Kwin, Emsley, Hedley, Sultan, Sulaiman, Gregary, Bindu, Brown, Allan, Mahmood, Afzal, Chattha, Navraj, Old, William, Pegg, Claire, Davey, Miriam, Page, Michelle, Sandhu, Banher, Phiri, Emily, Rashed, Khalid, Wilson, Elisabeth, Hindley, Esther, Board, Sarah, Antony, Sherly, Tanate, Alfonso, Davis, Michelle, Holland, Beth, Slater, Victoria, Fawcett, Michelle, England, Tim, Scott, James, Beavan, Jessica, Hedstrom, Amanda, Karunatilake, Dumin, Gillmain, Kimberley, Singh, Nishy, Hallows, Tracy, Barber, Mark, Yates, Luke, Micallef, Clayton, Esson, Derek, Meng Yu, Wai, Ming New, Benjamin Jaa, Matos, Alexandre, Burt, Clare, Cabrelli, Louise, Wilkie, Gillian, Meegada, Madana, Kirthivasan, Ramanathan, Fox, Caroline, Mead, Victoria, Lyle, Amanda, Saksena, Rajesh, Bakshi, Aashima, O'Kelly, Alison, Rehan, Jahanzeb, Ebueka, Osaretin, Cooper, Martin, Wynter, Inez, Smith, Susan, Kumar, Senthil, O'Brien, Linda, Parker, Cerrys, Parker, Emma, Khan, Numan, Patterson, Christopher, Maguire, Stuart, Quinn, Outi, Bellfield, Ruth, Behnam, Yousif, Costa, Janet, Padilla-Harris, Cheryl, Moram, Louise, Raza, Syed Abid, Tench, Helen, Sims, Tanya, McGuinness, Heather, Loosley, Ronda, Wolf-Roberts, Rebecca, Buddha, Sandeep, Salt, Irmak, Lewis, Kerry, Mavinamne, Sunanda, Ditchfield, Coleen, Dealing, Sharon, Shah, Alexander, Crossingham, Ginette, Mwadeyi, Memory, Kenton, Anthony, Omoregie, Faith, Abubakar, Saidu, Warwick, Allison, Hector, Gemma, Hassan, Ahamad, Veraque, Emelda, Farman, Michelle, Makawa, Linetty, Byrne, Anthony, Kirkham, Jackie, Blayney, Gareth, Selwyn, Jey, Kakar, Puneet, Al Khaddour, Mohammed, Dhami, Reena, Baker, Emelda, Esisi, Bernard, Clarkson, Emma, Fellowes, Dominic, Kresmir, Jergovic, Guyler, Paul, Ngo, David, Wijenayake, Indunil, Tysoe, Sharon, Galliford, Joanne, Harman, Paula, Garside, Mark, Badanahatti, Madhava, Riddell, Victoria, Gramizadeh, Gita, Dutta, Dipankar, Bajoriene, Milda, Erdogan, Hulya, Ward, Deborah, Doubal, Fergus, Samarasekera, Neshika, Risbridger, Sarah, MacRaild, Allan, Azim, Abul, Wood, Lisa, Tampset, Ruth, Shekhar, Raj, Rai, Umesh, Fuller, Tracy, Joshy, Aricsa, Nadar, Evelyn, Kini, Manohar, Ahmad, Syed, Robinson, Matthew, King, Lucia, Srinivasan, Venkatesan, Karwacka-Cichomska, Magdalena, Moore, Vicki, Smith, Kate, Kariyadil, Bincy, Kong, Kelvin, Hubbard, Kelly, Arif, Sarwat, Hasan, Muhammad, Temple, Natalie, Arcoria, Daniele, Horne, Zoey, Soe, Thandar, Wyllie, Hilary, Hacon, Christian, Sutherland, Helen, Menezes, Brian, Johnson, Venetia, Smyth, Nigel, Mehdi, Zehra, Tone, Ela, Bradley, Arian, Levell, Emma, Ekkert, Aleksandra, Mazzucco, Sara, McCafferty, Laura, Vonoven, Linda, Dewan, Suprita, Sridhar, Pagadala, Thomas, Jayne, Coetzee, Samantha, Icke, Becky, Williams, Jill, Saravanan, Narayanamoorthi, Bradley, Pamela, Gibson, Rebecca Marie, Antony, Jijimol, Ashraf, Imran, Mabuti, Jose, Kamundi, Charlotte, Patiola, Prasanna, Oakley, Naomi, Proeschel, Harold, Kelly, Debs, Longley, Wendy, Cave, Ashleigh, Ambrico, Carla, Black, Toby, Porretta, Elisa, Anthony, Alpha, Ragab, Suzanne, Dube, Judith, Kausar, Shahid, Gujjar, Abdullah, Abdullah, Mohammad, Kaur, Daljit, Gadapa, Naveen, Choudhary, Sumita, Nisar, Nabeela, Fawehinmi, Grace, Dunne, Karen, King, Sam, Kishore, Amit, Lee, Stephanie, Marsden, Tracy, Slaughter, Melanie, Cawley, Kathryn, Perez, Jane, Anderton, Peter, Soussi, Salem, Walstow, Deborah, Pugh, Rebecca, Manoj, Aravind, Fletcher, Glynn, Lopez, Paula, McCormick, Michael, Magee, Michael, Tallon, Grainne, McFarland, Denise, Cosgrove, Denise, Shinh, Naval, Metcalf, Kneale, Kostyuk, Alina, McDonald, Susan, Sayers, Sophie, Sayed, Walee, Abraham, Sam, Szabo, Gemma, Crosbie, Gareth, McIlmoyle, Jim, Fearon, Patricia, Courtney, Kerry, Tauro, Suzanne, Singh, Arun, Nair, Anand, Duberley, Stephen, Philip, Sheeba, Curley, Cath, Goddard, Wendy, Bridge, Luke, Willcoxson, Paul, Wanklyn, Peter, Owen, Jennifer, France, John, Reed, Bryony, Foulds, Angie, Richard, Bella, Parfitt, Louise, Affley, Brendan, Russo, Cristina, Dsouza, Margaret, Cruddas, Elizabeth, Hargroves, David, Rand, James, Shekar, Som, Bhat, Yaqoob, Marshall, Gail, Nash, Maxine, Ahmad, Nasar, Okoko, Blessing Oduh, Evans, Rachel, Taylor, Tegan, Dawson, Jesse, Colquhoun, Elizabeth, James, Christopher, Aguirre, Carlos, MacPhee, Catherine, Phipps, Janet, Ispoglou, Sissi, Hayes, Anne, Werring, David J, Dehbi, Hakim-Moulay, Ahmed, Norin, Arram, Liz, Best, Jonathan G, Balogun, Maryam, Bennett, Kate, Bordea, Ekaterina, Caverly, Emilia, Chau, Marisa, Cohen, Hannah, Cullen, Mairead, Doré, Caroline J, Engelter, Stefan T, Fenner, Robert, Ford, Gary A, Gill, Aneet, Hunter, Rachael, James, Martin, Jayanthi, Archana, Lip, Gregory Y H, Massingham, Sue, Murray, Macey L, Mazurczak, Iwona, Nash, Philip S, Ndoutoumou, Amalia, Norrving, Bo, Sims, Hannah, Sprigg, Nikola, Vanniyasingam, Tishok, and Freemantle, Nick

Published
2024
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6. Latent Transformer Models for out-of-distribution detection

Author

Graham, Mark S., Tudosiu, Petru-Daniel, Wright, Paul, Pinaya, Walter Hugo Lopez, Teikari, Petteri, Patel, Ashay, U-King-Im, Jean-Marie, Mah, Yee H., Teo, James T., Jäger, Hans Rolf, Werring, David, Rees, Geraint, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge

Published
2023
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7. Unsupervised 3D Out-of-Distribution Detection with Latent Diffusion Models

Author

Graham, Mark S., primary, Pinaya, Walter Hugo Lopez, additional, Wright, Paul, additional, Tudosiu, Petru-Daniel, additional, Mah, Yee H., additional, Teo, James T., additional, Jäger, H. Rolf, additional, Werring, David, additional, Nachev, Parashkev, additional, Ourselin, Sebastien, additional, and Cardoso, M. Jorge, additional

Published
2023
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8. Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models

Author

Pinaya, Walter H. L., primary, Graham, Mark S., additional, Gray, Robert, additional, da Costa, Pedro F., additional, Tudosiu, Petru-Daniel, additional, Wright, Paul, additional, Mah, Yee H., additional, MacKinnon, Andrew D., additional, Teo, James T., additional, Jager, Rolf, additional, Werring, David, additional, Rees, Geraint, additional, Nachev, Parashkev, additional, Ourselin, Sebastien, additional, and Cardoso, M. Jorge, additional

Published
2022
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10. Optimal timing of anticoagulation after acute ischaemic stroke with atrial fibrillation (OPTIMAS): a multicentre, blinded-endpoint, phase 4, randomised controlled trial

Author

Werring, David J, Dehbi, Hakim-Moulay, Ahmed, Norin, Arram, Liz, Best, Jonathan G, Balogun, Maryam, Bennett, Kate, Bordea, Ekaterina, Caverly, Emilia, Chau, Marisa, Cohen, Hannah, Cullen, Mairead, Doré, Caroline J, Engelter, Stefan T, Fenner, Robert, Ford, Gary A, Gill, Aneet, Hunter, Rachael, James, Martin, Jayanthi, Archana, Lip, Gregory Y H, Massingham, Sue, Murray, Macey L, Mazurczak, Iwona, Nash, Philip S, Ndoutoumou, Amalia, Norrving, Bo, Sims, Hannah, Sprigg, Nikola, Vanniyasingam, Tishok, Freemantle, Nick, Jelley, Benjamin, Hughes, Tom, Evans, Mim, Esteban, Diego Garcia, Knibbs, Lucy, Broad, Lauren, Price, Rebecca, Griebel, Liz Hamer, Hewson, Sian, Thavanesan, Kamy, Mallon, Louise, Smith, Anna, White, Miranda, Zhang, Liqun, Clarke, Brian, Abousleiman, Youssif, Binnie, Lauren, Sim, Cai Hua, Castanheira, Margarida, Humphries, Fiona, Obarey, Sabaa, Feerick, Shez, Lee, Yee Chin, Lewis, Alex, Muhammad, Riham, Francia, Nina, Atang, Ndifreke, Banaras, Azra, Marinescu, Marilena, Ferdinand, Philip, Varquez, Resti, Ponce, Ida, Saxena, Surabhi, O'Brien, Eoin, Reyes, Juliana Delos, Mitchell-Douglas, Jennifer, Francis, Jobbin, Banerjee, Soma, Dave, Vaishali, Mashate, Sheila, Patel, Tulsi, Sekaran, Lakshmanan, Murad, Wahid, Asaipillai, Asokanathan, Sakthivel, Sethuraman, Tate, Margaret, Angus, Jane, Reid, Lisa, Fornolles, Caroline, Sundayi, Saul, Poolon, Lincy, Justin, Francis, Hunte, Sophy, Bhandari, Mohit, Kho, Jules, Cvoro, Vera, Parakramawansha, Ruwan, Couser, Mandy, Hughes, Hannah, Naqvi, Aaizza, Harkness, Kirsty, Richards, Emma, Howe, Jo, Kamara, Chris, Gardner, Jon, Bains, Harjit, Teal, Rachel, Joseph, Jeethu, Benjamin, Jithen, Al-Hussayni, Samer, Thomas, George, Robinson, Faye, Dixon, Lynn, Krishnan, Manju, Slade, Peter, Anjum, Tal, Storton, Sharon, Adie, Katja, Northcott, Keren, Morgan, Katie, Williams, Emilie, Chanashekar, Harinath, Maguire, Holly, Gabriel, Claire, Maren, Deborah, David, Hannah, Clarke, Sheron, Nagaratnam, Kiruba, Nelatur, Varun, Mannava, Neelima, Blasco, Lara, Devine, Joseph, Bathula, Rajaram, Gopi, Parvathy, Mehta, Niharika, Sreedevi Raj, Sreena, Teo, James, Sztriha, Laszio, Mah, Yee, Ankolekar, Sandeep, Sari, Beatrix, Tibajai, Maria, Morgan, Alicia, Recaman, Maria, Bayhonan, Samantha, Belo, Caroline, Finch, Sharon, Keenan, Samantha, Bowring, Angie, Shetty, Ashit, Chan, Siang, Gray, Lucy, Harrison, Thomas, Spooner, Oliver, Kinsella-Perks, Edward, Erumere, Esther, Sanders, Brittany, Sims, Don, Willmot, Mark, Littleton, Edward, Spruce, Elaine, Moody, Lisa, Sheriden, Christopher, Luxmore-Brown, Scott, Neal, Aoife, Beddows, Sophie, Tuna, Maria Assuncao, Misra, Amulya, Penn, Ruth, Mariampillai, Sonia, Anwar, Ijaz, Annamalai, Arunkumar, Whitehouse, Sarah, Shepherd, Lorna, Siddle, Elaine, Chatterjee, Kausik, Leason, Sandra, Davies, Angela, Marigold, Richard James, Frank, Sarah, Baird, Alix, Hannam-Penfold, Tomas, Inacio, Liliana, Smith, Simon, Eveson, David, Musarrat, Kashif, Khan, Shagufta, Harris, Tracy, Chowdhury, Muhibbur, Alam, Sajid, Jamieson, Elena, Anyankpele, Ebitare, Al Shalchi, Farah, Rivers, Vanessa, Bell, Stephanie, Francis, Rebecca, Beeby, Deborah, Finch, Jenny, Macleod, Mary Joan, Guzman-Gutierrez, German, Carter, Karla, Irvine, Janice, Gbadamoshi, Lukuman, Costa, Telma, Heirons, Sarah, Stoney, Hayley, Shaw, Louise, Choulerton, James, Catibog, Darwin, Sattar, Naweed, Myint, Min, Smith, Andy, Serac, Kwin, Emsley, Hedley, Sultan, Sulaiman, Gregary, Bindu, Brown, Allan, Mahmood, Afzal, Chattha, Navraj, Old, William, Pegg, Claire, Davey, Miriam, Page, Michelle, Sandhu, Banher, Phiri, Emily, Rashed, Khalid, Wilson, Elisabeth, Hindley, Esther, Board, Sarah, Antony, Sherly, Tanate, Alfonso, Davis, Michelle, Holland, Beth, Slater, Victoria, Fawcett, Michelle, England, Tim, Scott, James, Beavan, Jessica, Hedstrom, Amanda, Karunatilake, Dumin, Gillmain, Kimberley, Singh, Nishy, Hallows, Tracy, Barber, Mark, Yates, Luke, Micallef, Clayton, Esson, Derek, Meng Yu, Wai, Ming New, Benjamin Jaa, Matos, Alexandre, Burt, Clare, Cabrelli, Louise, Wilkie, Gillian, Meegada, Madana, Kirthivasan, Ramanathan, Fox, Caroline, Mead, Victoria, Lyle, Amanda, Saksena, Rajesh, Bakshi, Aashima, O'Kelly, Alison, Rehan, Jahanzeb, Ebueka, Osaretin, Cooper, Martin, Wynter, Inez, Smith, Susan, Kumar, Senthil, O'Brien, Linda, Parker, Cerrys, Parker, Emma, Khan, Numan, Patterson, Christopher, Maguire, Stuart, Quinn, Outi, Bellfield, Ruth, Behnam, Yousif, Costa, Janet, Padilla-Harris, Cheryl, Moram, Louise, Raza, Syed Abid, Tench, Helen, Sims, Tanya, McGuinness, Heather, Loosley, Ronda, Wolf-Roberts, Rebecca, Buddha, Sandeep, Salt, Irmak, Lewis, Kerry, Mavinamne, Sunanda, Ditchfield, Coleen, Dealing, Sharon, Shah, Alexander, Crossingham, Ginette, Mwadeyi, Memory, Kenton, Anthony, Omoregie, Faith, Abubakar, Saidu, Warwick, Allison, Hector, Gemma, Hassan, Ahamad, Veraque, Emelda, Farman, Michelle, Makawa, Linetty, Byrne, Anthony, Kirkham, Jackie, Blayney, Gareth, Selwyn, Jey, Kakar, Puneet, Al Khaddour, Mohammed, Dhami, Reena, Baker, Emelda, Esisi, Bernard, Clarkson, Emma, Fellowes, Dominic, Kresmir, Jergovic, Guyler, Paul, Ngo, David, Wijenayake, Indunil, Tysoe, Sharon, Galliford, Joanne, Harman, Paula, Garside, Mark, Badanahatti, Madhava, Smith, Anna, Riddell, Victoria, Gramizadeh, Gita, Dutta, Dipankar, Bajoriene, Milda, Erdogan, Hulya, Ward, Deborah, Doubal, Fergus, Samarasekera, Neshika, Risbridger, Sarah, MacRaild, Allan, Azim, Abul, Wood, Lisa, Tampset, Ruth, Shekhar, Raj, Rai, Umesh, Fuller, Tracy, Joshy, Aricsa, Nadar, Evelyn, Kini, Manohar, Ahmad, Syed, Robinson, Matthew, King, Lucia, Srinivasan, Venkatesan, Karwacka-Cichomska, Magdalena, Moore, Vicki, Smith, Kate, Kariyadil, Bincy, Kong, Kelvin, Hubbard, Kelly, Arif, Sarwat, Hasan, Muhammad, Temple, Natalie, Arcoria, Daniele, Horne, Zoey, Soe, Thandar, Wyllie, Hilary, Hacon, Christian, Sutherland, Helen, Menezes, Brian, Johnson, Venetia, Smyth, Nigel, Mehdi, Zehra, Tone, Ela, Bradley, Arian, Levell, Emma, Ekkert, Aleksandra, Mazzucco, Sara, McCafferty, Laura, Vonoven, Linda, Dewan, Suprita, Sridhar, Pagadala, Thomas, Jayne, Coetzee, Samantha, Icke, Becky, Williams, Jill, Saravanan, Narayanamoorthi, Bradley, Pamela, Gibson, Rebecca Marie, Antony, Jijimol, Ashraf, Imran, Mabuti, Jose, Kamundi, Charlotte, Patiola, Prasanna, Oakley, Naomi, Proeschel, Harold, Kelly, Debs, Longley, Wendy, Cave, Ashleigh, Ambrico, Carla, Black, Toby, Porretta, Elisa, Anthony, Alpha, Ragab, Suzanne, Dube, Judith, Kausar, Shahid, Gujjar, Abdullah, Abdullah, Mohammad, Kaur, Daljit, Gadapa, Naveen, Choudhary, Sumita, Nisar, Nabeela, Fawehinmi, Grace, Dunne, Karen, King, Sam, Kishore, Amit, Lee, Stephanie, Marsden, Tracy, Slaughter, Melanie, Cawley, Kathryn, Perez, Jane, Anderton, Peter, Soussi, Salem, Walstow, Deborah, Pugh, Rebecca, Manoj, Aravind, Fletcher, Glynn, Lopez, Paula, McCormick, Michael, Magee, Michael, Tallon, Grainne, McFarland, Denise, Cosgrove, Denise, Shinh, Naval, Metcalf, Kneale, Kostyuk, Alina, McDonald, Susan, Sayers, Sophie, Sayed, Walee, Abraham, Sam, Szabo, Gemma, Crosbie, Gareth, McIlmoyle, Jim, Fearon, Patricia, Courtney, Kerry, Tauro, Suzanne, Singh, Arun, Nair, Anand, Duberley, Stephen, Philip, Sheeba, Curley, Cath, Goddard, Wendy, Bridge, Luke, Willcoxson, Paul, Wanklyn, Peter, Owen, Jennifer, France, John, Reed, Bryony, Foulds, Angie, Richard, Bella, Parfitt, Louise, Affley, Brendan, Russo, Cristina, Dsouza, Margaret, Cruddas, Elizabeth, Hargroves, David, Rand, James, Shekar, Som, Bhat, Yaqoob, Marshall, Gail, Nash, Maxine, Ahmad, Nasar, Okoko, Blessing Oduh, Evans, Rachel, Taylor, Tegan, Dawson, Jesse, Colquhoun, Elizabeth, James, Christopher, Aguirre, Carlos, MacPhee, Catherine, Phipps, Janet, Ispoglou, Sissi, Hayes, Anne, and Evans, Rachel

Abstract

The optimal timing of anticoagulation for patients with acute ischaemic stoke with atrial fibrillation is uncertain. We investigated the efficacy and safety of early compared with delayed initiation of direct oral anticoagulants (DOACs) in patients with acute ischaemic stroke associated with atrial fibrillation.

Published
2024
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11. Antiepileptogenesis after stroke—trials and tribulations: Methodological challenges and recruitment results of a Phase II study with eslicarbazepine acetate

Author

Koepp, Matthias J., primary, Trinka, Eugen, additional, Mah, Yee‐Haur, additional, Bentes, Carla, additional, Knake, Susanne, additional, Gigli, Gian Luigi, additional, Serratosa, José M., additional, Zelano, Johan, additional, Magalhães, Luís M., additional, Pereira, Ana, additional, Moreira, Joana, additional, and Soares‐da‐Silva, Patrício, additional

Published
2023
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15. Neuradicon: operational representation learning of neuroimaging reports

Author

Watkins, Henry, Gray, Robert, Julius, Adam, Mah, Yee-Haur, Pinaya, Walter H. L., Wright, Paul, Jha, Ashwani, Engleitner, Holger, Cardoso, Jorge, Ourselin, Sebastien, Rees, Geraint, Jaeger, Rolf, Nachev, Parashkev, Watkins, Henry, Gray, Robert, Julius, Adam, Mah, Yee-Haur, Pinaya, Walter H. L., Wright, Paul, Jha, Ashwani, Engleitner, Holger, Cardoso, Jorge, Ourselin, Sebastien, Rees, Geraint, Jaeger, Rolf, and Nachev, Parashkev

Abstract

Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis. This limits the monitoring of radiological services to throughput undifferentiated by content, impeding specific, targeted operational optimization. Here we present Neuradicon, a natural language processing (NLP) framework for quantitative analysis of neuroradiological reports. Our framework is a hybrid of rule-based and artificial intelligence models to represent neurological reports in succinct, quantitative form optimally suited to operational guidance. We demonstrate the application of Neuradicon to operational phenotyping of a corpus of 336,569 reports, and report excellent generalizability across time and two independent healthcare institutions., Comment: 26 pages, 11 figures

Published
2021

21. Rates, risks and routes to reduce vascular dementia (R4vad), a UK-wide multicentre prospective observational cohort study of cognition after stroke: Protocol.

Author

Wardlaw, Joanna M, Doubal, Fergus, Brown, Rosalind, Backhouse, Ellen, Woodhouse, Lisa, Bath, Philip, Quinn, Terence J, Robinson, Thompson, Markus, Hugh S, McManus, Richard, O'Brien, John T, Werring, David J, Sprigg, Nikola, Parry-Jones, Adrian, Touyz, Rhian M, Williams, Steven, Mah, Yee-Haur, and Emsley, Hedley

Published
2021
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22. A brain origin for factitious disorder (Munchausen's) with malingering? A single case with an old frontal lobe lesion.

Author

Coebergh, Jan Adriaan, Amlani, Aliza, Edwards, Mark, Mah, Yee-Haur, and Agrawal, Niruj

Subjects
FRONTAL lobe, MALINGERING, PERSONALITY assessment, BRAIN damage, SYMPTOMS, FRONTAL lobe diseases
Abstract

We describe a patient who presented with gender identity dysphoria and stroke-like symptoms who we diagnosed with Munchausen's syndrome (factitious disorder). We discuss whether a brain lesion in the left frontal cortex is a possible risk factor, and support this hypothesis through neuropsychological investigation, EEG abnormalities, and a personality assessment. This case report supports previous suggestions that underlying brain disease/lesions might be risk factors for Munchausen's syndrome (factitious disorder). [ABSTRACT FROM AUTHOR]

Published
2021
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24. Cognition and mood in the first few months after stroke: relationship to stroke severity and dependency

Author

Backhouse, Ellen V, Woodhouse, Lisa J, Doubal, Fergus, Brown, Rosalind, Bath, Philip M, Quinn, Terence J, Robinson, Thompson, Markus, Hugh S, McManus, Richard J, O'Brien, John T, Werring, David J, Sprigg, Nikola, Parry-Jones, Adrian, Touyz, Rhian M, Williams, Steven, Mah, Yee-Haur, Emsley, Hedley, and Wardlaw, Joanna M

Abstract

Cognitive decline and mood disorders are two major concerns of people affected by stroke. The extent to which cognition and mood relate to or are independent of stroke severity and post-stroke dependency are unclear. We examined the associations between stroke severity, global measures of cognition, mood and dependency up to 14-weeks post-stroke in a large national study of neurocognitive complications of stroke up to two years, the Rates, Risks and Routes to Reduce Vascular Dementia (R4VaD) study.

Published
2024
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27. Dural arteriovenous fistula causing reversible cognitive impairment.

Author

Alexandratou A, Mah Y, Ramsey D, Kandasamy N, Tolias CM, Gadapa N, and Ankolekar S

Subjects
Female, Humans, Aged, 80 and over, Confusion, Embolization, Therapeutic, Dementia etiology, Central Nervous System Vascular Malformations complications, Central Nervous System Vascular Malformations diagnostic imaging, Central Nervous System Vascular Malformations therapy, Cognitive Dysfunction diagnostic imaging, Cognitive Dysfunction etiology
Abstract

A previously independent 82-year-old woman presented with 5 months of worsening confusion, mobility and cognitive decline, with deficits in orientation, language and executive function. A cerebral dural arteriovenous fistula was identified and successfully embolised, after which her cognitive ability and independence dramatically improved. Although rare, a dural arteriovenous fistula may mimic a rapidly progressive dementia, but its early recognition and treatment can completely reverse the dementia., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Published
2023
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