168 results on '"Tsoulfas, George"'
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2. Streamlining Tangible 3D Printed and Intangible XR Content Creation and Evaluation: The ENTICE Experience
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Antoniou, Panagiotis, primary, Sidiropoulos, Efstathios, additional, Stathakarou, Natalia, additional, Chatzimallis, Charalampos, additional, Chondrokostas, Evangelos, additional, Sumunar, Dimas, additional, Karlsson, Tobias, additional, Lachanoudi, Sofia, additional, David, Panagiotis, additional, Tagaras, Konstantinos, additional, Varella, Annita, additional, Athanasiou, Alkinoos, additional, Pickering, James, additional, Tooulias, Andreas, additional, Karolos, Ion, additional, Filippidis, Panagiotis-Marios, additional, Schiza, Eirini, additional, Voulgarakis, Vassilis, additional, Bratsas, Charalambos, additional, Tsioukas, Vassilis, additional, Tsoulfas, George, additional, and Bamidis, Panagiotis, additional
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- 2023
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3. Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0
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Agarwal, Mohit, Agarwal, Sushant, Saba, Luca, Chabert, Gian Luca, Gupta, Suneet, Carriero, Alessandro, Pasche, Alessio, Danna, Pietro, Mehmedovic, Armin, Faa, Gavino, Shrivastava, Saurabh, Jain, Kanishka, Jain, Harsh, Jujaray, Tanay, Singh, Inder M., Turk, Monika, Chadha, Paramjit S., Johri, Amer M., Khanna, Narendra N., Mavrogeni, Sophie, Laird, John R., Sobel, David W., Miner, Martin, Balestrieri, Antonella, Sfikakis, Petros P., Tsoulfas, George, Misra, Durga Prasanna, Agarwal, Vikas, Kitas, George D., Teji, Jagjit S., Al-Maini, Mustafa, Dhanjil, Surinder K., Nicolaides, Andrew, Sharma, Aditya, Rathore, Vijay, Fatemi, Mostafa, Alizad, Azra, Krishnan, Pudukode R., Yadav, Rajanikant R., Nagy, Frence, Kincses, Zsigmond Tamás, Ruzsa, Zoltan, Naidu, Subbaram, Viskovic, Klaudija, Kalra, Manudeep K., and Suri, Jasjit S.
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- 2022
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4. The Role of Surgery in Treating Parasitic Diseases of the Gastrointestinal Tract from Protozoa
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Ziogas, Ioannis A., Tsoulfas, George, Tsoulfas, George, editor, Hoballah, Jamal J., editor, Velmahos, George C., editor, and Ho, Yik-Hong, editor
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- 2020
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5. Basics of Trauma Management Abdominal Trauma
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Tsoulfas, George, Coccolini, Federico, Series Editor, Coimbra, Raul, Series Editor, Kirkpatrick, Andrew W., Series Editor, Di Saverio, Salomone, Series Editor, Ansaloni, Luca, Editorial Board Member, Balogh, Zsolt, Editorial Board Member, Biffl, Walt, Editorial Board Member, Catena, Fausto, Editorial Board Member, Davis, Kimberly, Editorial Board Member, Ferrada, Paula, Editorial Board Member, Fraga, Gustavo, Editorial Board Member, Ivatury, Rao, Editorial Board Member, Kluger, Yoram, Editorial Board Member, Leppaniemi, Ari, Editorial Board Member, Maier, Ron, Editorial Board Member, Moore, Ernest E., Editorial Board Member, Napolitano, Lena, Editorial Board Member, Peitzman, Andrew, Editorial Board Member, Reilly, Patrick, Editorial Board Member, Rizoli, Sandro, Editorial Board Member, Sakakushev, Boris E., Editorial Board Member, Sartelli, Massimo, Editorial Board Member, Scalea, Thomas, Editorial Board Member, Spain, David, Editorial Board Member, Stahel, Philip, Editorial Board Member, Sugrue, Michael, Editorial Board Member, Velmahos, George, Editorial Board Member, Weber, Dieter, Editorial Board Member, Pikoulis, Emmanouil, editor, and Doucet, Jay, editor
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- 2021
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6. A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence
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Suri, Jasjit S., Agarwal, Sushant, Gupta, Suneet K., Puvvula, Anudeep, Biswas, Mainak, Saba, Luca, Bit, Arindam, Tandel, Gopal S., Agarwal, Mohit, Patrick, Anubhav, Faa, Gavino, Singh, Inder M., Oberleitner, Ronald, Turk, Monika, Chadha, Paramjit S., Johri, Amer M., Miguel Sanches, J., Khanna, Narendra N., Viskovic, Klaudija, Mavrogeni, Sophie, Laird, John R., Pareek, Gyan, Miner, Martin, Sobel, David W., Balestrieri, Antonella, Sfikakis, Petros P., Tsoulfas, George, Protogerou, Athanasios, Misra, Durga Prasanna, Agarwal, Vikas, Kitas, George D., Ahluwalia, Puneet, Teji, Jagjit, Al-Maini, Mustafa, Dhanjil, Surinder K., Sockalingam, Meyypan, Saxena, Ajit, Nicolaides, Andrew, Sharma, Aditya, Rathore, Vijay, Ajuluchukwu, Janet N.A., Fatemi, Mostafa, Alizad, Azra, Viswanathan, Vijay, Krishnan, P.K., and Naidu, Subbaram
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- 2021
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7. COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review
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Suri, Jasjit S., Puvvula, Anudeep, Biswas, Mainak, Majhail, Misha, Saba, Luca, Faa, Gavino, Singh, Inder M., Oberleitner, Ronald, Turk, Monika, Chadha, Paramjit S., Johri, Amer M., Sanches, J. Miguel, Khanna, Narendra N., Viskovic, Klaudija, Mavrogeni, Sophie, Laird, John R., Pareek, Gyan, Miner, Martin, Sobel, David W., Balestrieri, Antonella, Sfikakis, Petros P., Tsoulfas, George, Protogerou, Athanasios, Misra, Durga Prasanna, Agarwal, Vikas, Kitas, George D., Ahluwalia, Puneet, Kolluri, Raghu, Teji, Jagjit, Maini, Mustafa Al, Agbakoba, Ann, Dhanjil, Surinder K., Sockalingam, Meyypan, Saxena, Ajit, Nicolaides, Andrew, Sharma, Aditya, Rathore, Vijay, Ajuluchukwu, Janet N.A., Fatemi, Mostafa, Alizad, Azra, Viswanathan, Vijay, Krishnan, Pudukode R., and Naidu, Subbaram
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- 2020
- Full Text
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8. Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study
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Nepogodiev, Dmitri, Bhangu, Aneel, Glasbey, James C, Li, Elizabeth, Omar, Omar M, Simoes, Joana FF, Abbott, Tom EF, Alser, Osaid, Arnaud, Alexis P, Bankhead-Kendall, Brittany K, Breen, Kerry A, Cunha, Miguel F, Davidson, Giana H, Di Saverio, Salomone, Gallo, Gaetano, Griffiths, Ewen A, Gujjuri, Rohan R, Hutchinson, Peter J, Kaafarani, Haytham MA, Lederhuber, Hans, Löffler, Markus W, Mashbari, Hassan N, Minaya-Bravo, Ana, Morton, Dion G, Moszkowicz, David, Pata, Francesco, Tsoulfas, George, Venn, Mary L, Cox, Daniel, Roslani, April C, Alakaloko, Felix, de Vries, Jean-Paul PM, Aaraj, Mahmoud A, Abbott, Sarah J, Abdalla, Mutwakil OM, Abdelaal, Ahmed S, Ademuyiwa, Adesoji O, Aherne, Thomas M, Ali, Osman M, Alkadeeki, Ghadah Z, Almeida, Ana C, Alrahawy, Mahmoud M, Ambler, Graeme K, Alameer, Ehab, Andreani, Stefano M, De Andrés-Asenjo, Beatriz, Antonanzas, Leyre Lopez, Aoun, Salah G, Ashoush, Fouad M, Augestad, Knut Magne, Avellana, Rocio B, Ayeni, Funbi A, Ayorinde, John OO, Babu, Bheemanakone H, Baig, Mirza MAS, Bajomo, Oreoluwa M, Baker, Olivia J, Baker, Markus P, Baldwin, Alexander J, Ban, Vin Shen, Baron, Ryan D, Barranquero, Alberto G, Barry, Conor P, DI Bartolomeo, Alessandro, Bass, Gary A, Bath, Michael F, Batjer, H Hunt, Beamish, Andrew J, Belgaumkar, Ajay P, Bence, Matthew N, Benson, Ruth A, Bernal-Sprekelsen, Juan Carlos, Bhama, Anuradha R, Bhavaraju, Avi V, Biffl, Walter L, Blundell, Chris M, Boddy, Alexander P, Borgstein, Alexander BJ, Bosanquet, David C, Bosch, Karen D, Bouhuwaish, Ahmad EM, Bozkurt, Mehmet A, Brathwaite, Collin EM, Brown, Benjamin C, Brown, Oliver D, Brown, Allison K, Buarque, Igor Lima, Bueno-Cañones, Alejandro D, Bulugma, Mustafa R, Burke, Joshua R, Byrne, Matthew HV, Cagigal-Ortega, Elima P, Callcut, Rachael A, DI Candido, Francesca, Canova, Michaela E, Carlos, William J, Caruana, Edward J, Cato, Liam D, Catton, Andrew B, Ceretti, Andrea Pisani, Chase, Thomas JG, Chiara, Francesco Di, Chowdhury, Abeed H, Chung, Eric A, Cicerchia, Pierfranco M, Clough, Ethan CS, Coleman, Natasha L, Collins, Chris G, Collins, Michelle L, Colonna, Emily T, Comini, Lara V, Coughlin, Patrick A, Cruzado, Laura Fernández-Gomez, Davidson, Brian R, Davies, Richard J, Davies, Emma J, Davis, Niall F, Dawson, Brett E, Dean, Benjamin JF, Delgado, Maria Garcia-Conde, Diaz, Jose J, Dickson, Kathryn E, Diez-Alonso, Manuel M, Dixon, Jan R, Doe, Matthew J, Drake, Thomas D, Drake, Frederick T, Duffy, John P, Dunne, Declan FJ, Dunne, Naomi JM, Durán-Muñoz-Cruzado, Virginia M, Durst, Alexander ZE, Eardley, Nicola J, Edwards, John G, Elfallal, Ahmed H, Elfiky, Mahmoud MA, Elliott, Jessie A, Emile, Sameh H, Emslie, Katy M, Endorf, Frederick W, Engel, Jamie L, Enjuto, Diego T, Etchill, Eric W, Evans, Jonathan P, Fahey, Brian A, Faria, Carlos S, Feo, Carlo V, Ferguson, Henry JM, Fernandez, Beatriz Dieguez, Fernandez, Andres Garcia, Fernández, Antonio J, Fernández-Pacheco, Borja Camacho, Fitzgerald, J Edward, Fonsi, Giovanni B, Font, Roser Farré, Fowler, Amy L, Fretwell, Kenneth R, Fructuoso, Lorena Sanchon, Fusai, Giuseppe K, Garcia, Miguel Hernandez, Garcia-Ureña, Miguel Angel, Gill, Charn K, Gisbertz, Suzanne S, Del Giudice, Roberto, Giuffrida, Maria Carmela, Di Giuseppe, Matteo, Gómez, María Fanjul, Guariglia, Claudio A, Hainsworth, Alison J, Hall, Bria J, Hall, James RW, Hammond, John S, Haqqani, Maha H, Harrison, Ewen M, Hazelton, Joshua P, van Heinsbergen, Maarten, Hill, Arnold DK, Hing, Caroline B, Hirji, Sameer A, Ho, Michael WS, Holbrook, Charlotte M, Holme, Thomas J, Hopkins, James C, Hopkinson, David N, Hossain, Fahad S, Hudson, Victoria E, Hughes, Jane L, Hwang, E. Shelley, Ibrahim, Mohamed AH, Isolani, Simone M, Jenkinson, Michael D, Jenny, Hillary E, Jeyaretna, Deva S, Jones, Robert P, Jones, Andrew P, Jonker, Pascal KC, Jönsson, Maria L, Joyce, Doireann P, Kalkwarf, Kyle J, Kamarajah, Sivesh K, El Kassas, Mohamed, Kavanagh, Dara O, Keatley, James M, Khalefa, Mohamed A, Khan, Jim S, Kirmani, Bilal H, Kisiel, Aaron P, Kouris, Spyros Marinos, Kowal, Mikolaj R, Labib, Peter L, Larkin, John O, Lauscher, Johannes C, Leclercq, Wouter KG, Ledesma, Frances SJ, Leite-Moreira, André M, Leung, Elaine YL, Lewis, Sophia E, Lima, Maria João, Lin, Daniel J, Liu, Helen H, Lowery, Aoife J, Lozano, Saida Martel, Luney, Catriona R, Maia, Mariana Magalhães, Mariani, Nicolò M, Marino, Marco V, Marra, Angelo A, Marsh, Christopher L, Martin, Robert CG, McCluney, Simon J, McIntyre, Robert C, Mckay, Siobhan C, McKevitt, Kevin L, Meagher, Ashley D, Mehdi, Mohammad Q, Mehigan, Brian J, Gonzalez-De Miguel, Melania, De Miguel-Ardevines, Maria-Carmen, Mills, Sarah J, Mohan, Helen M, Moir, John AG, Monson, John RT, Monteiro, Joana M, Montella, Maria T, Montesinos, Cristina Soto, Morgom, Marwa M, Moura, Francisco S, Muguerza, Jose M, Murphy, Suzanne H, De Nardi, Paola, Naumann, David N, Neary, Paul C, Neely, David TA, Ng-Kamstra, Joshua S, Ngu, Albert WT, Nguyen, Truong A, Nita, George E, Nunes, Quentin M, Nygaard, Rachel M, O'Meara, Lindsay B, O'Neill, John R, Okafor, Barbara U, Olson, Steven A, Oo, Aung Y, Ormazabal, Pablo Collera, Osorio, Alexander L, Pachl, Max J, Parry, James T, Patel, Panna K, Pérez-Sánchez, Luis E, Pevidal, Ana Nogues, Pezzuto, Anna P, Philp, Matthew M, Pinkney, Thomas D, Pollok, Joerg M, Povey, Meical G, Poza, Alfredo Alonso, Rajgor, Amarkumar D, Rao, Jagan N, Raptis, Dimitri A, Rice, Henry E, Ridgway, Paul F, Rivas, Ana Munoz, Rodriguez-Sanjuan, Juan C, Rogers, Luke J, Da Roit, Anna, Rollett, Rebecca A, Romera, Jose L, Rooney, Siobhan M, Roxo, Vanessa I, Le Roy, Bertrand, Rubio, Eduardo E, Ruiz, Carolina Castro, Ruiz, Manuel Losada, Ryan, Éanna J, Saad, Abdel Rahman, Saeed, Samerah A, Salama, Hiba A, Salamah, Abdulrauf A, Sampietro, Gianluca M, Sarma, Diwakar R, Schaffer, Kathryn B, Schnitzbauer, Andreas A, Scurrah, Rachel J, Serevina, Olivia L, Serralheiro, Pedro A, Sewards, Joseph M, Shackcloth, Michael J, Shaw, Abigail V, Sheel, Andrea RG, Sica, Giuseppe S, De Simone, Veronica, Singh, Aminder A, Singh, Rabindra P, Skelly, Brendan L, Smith, Henry G, Sohail, Amir H, Spalding, Duncan R, Springford, Laurie R, Ssentongo, Anna E, Steinkamp, Pieter J, Stevens, Kent A, Stewart, Grant D, Stylianides, Nicholas A, Sullivan, Tom BB, Taher, Ahmed SA, Tamimy, Muhammad S, Tang, Alethea M, Tebala, Giovanni D, Tejero-Pintor, Francisco J, Thaha, Mohamed A, Thomas, Amy J, De Toma, Giorgio, La Torre, Filippo, Torres, Antonio J, Townshend, David N, Trout, Isobel M, Tucker, Sarah C, Ubhi, Harmony K, Vega, Viviana A, Velmahos, George C, Velopulos, Catherine G, Viswanath, Yirupaiahgari KS, Vivas, Alfredo A, Wade, Ryckie G, Wadley, Martin S, Wall, Joshua JS, Walters, Andrew M, Warren, Oliver J, Weerasinghe, Chamindri K, Wilkin, Richard JW, Williams, Katherine J, Winter, Stuart C, Wormald, Justin CR, Wright, Franklin L, Xyda, Souzana E, Young, Alastair L, Youssef, Mina MG, Yousuf, Farhat B, El Youzouri, Hanan, Zappa, Marco A, Abate, Emmanuele, Abdalaziz, Hossam, Abdelkarim, Mostafa, Abdou, Hossam, Aboelkassem-Ibrahim, Ahmad, Abuown, Ala, Acebes-Garcia, Fernando, Acharya, Metesh, Adamina, Michel, Addae-Boateng, Emmanuel, Aftab, Raiyyan, Agarwal, Arnav, Aguilar, José, Ahmed, Yousra, Aitken, Emma, Al-Azzawi, Marwa, Al-Embideen, Somya, Al-Masri, Mahmoud, Al-Najjar, Hani, Al-Sukaini, Ahmad, Alam, Ruhina, Alderson, Derek, Aliyeva, Zumrud, Aljanadi, Firas, Almasri, Murad, Alonso-Ortuño, Paula, Altintoprak, Fatih, Amira, Gamal, Amjad, Rabbia, Anania, Gabriele, Andabaka, Tatjana, Angelou, Dimitrios, Annamalai, Seethalakshmi, Annessi, Valerio, Anthoney, James, Anwar, Sibtain, Anwer, Mariyah, Aragon-Chamizo, Juan, Ardito, Antonella, Arigoni, Michele, Armao, Teodora, Arminio, Armando, Armstrong, Lara, Arnaud, Alexis, Asaad, Peter, Ashcroft, James, Ashmore, Christopher, Asqalan, Ahmad, Asti, Emanuele, Aubry, Emmanuelle, Aytac, Erman, Ayuso-Herrera, Esther, Baeza, Melody, Bailon-Cuadrado, Martin, Bakmaz, Bernarda, Baldi, Caterina, Baldini, Edoardo, Baldo, Stefano, Ballabio, Michele, Baloyiannis, Ioannis, Baltazar, Gerard, Bàmbina, Fabrizio, Bandiera, Alessandro, Barlow, Emma, Barmasse, Roberto, Barmpagianni, Christina, Baronio, Gianluca, Barra, Fabio, Bartsch, Anne-Marie, Basgaran, Amedra, Basha, Amr, Bashkirova, Varvara, Bastazza, Marco, Baumber, Rachel, Belcher, Elizabeth, Belvedere, Angela, Benítez-Linero, Inmaculada, Bergeat, Damien, Bernasconi, Matteo, Bhalla, Ashish, Bhutiani, Neal, Bianco, Federica, Bisagni, Pietro, Blake, Iain, Blanco-Colino, Ruth, Blazquez-Martin, Alma, Boal, Matthew, Bonavina, Luigi, Bonavina, Giulia, Bond-Smith, Giles, Booth, Karen, Borges, Filipe, Borghi, Felice, Bouchagier, Konstantinos, Bourke, Grainne, Boyle, Emily, Brachini, Gioia, Brain, Jessie, Brar, Amanpreet, Breckles, Lisa, Bretagnol, Frédéric, Brixton, Genevieve, Bruzzaniti, Placido, Bueser, Teofila, Burnside, Nathan, Caballero, Albert, Calcerrada-Alises, Enrique, Callahan, Miriam, Camarero, Enrique, Campagnaro, Tommaso, Campanelli, Michela, Candiani, Massimo, Cantalejo-Diaz, Miguel, Cao, Han, Capelli, Patrizio, Capizzi, Vita, Carcano, Giulio, Carissimi, Francesca, Carlini, Massimo, Carlucci, Michele, Carmichael, Heather, Carrasco, Milagros, Carrillo, Mariana, Carvello, Michele, Casati, Massimiliano, Castoro, Carlo, Catalan, Vanesa, Cavaleiro, Salomé, Cellerino, Paola, Centinaio, Giovanna, Cernei, Cristina, Cerro, Cristina, Cervellera, Maurizio, Chakrabortee, Sohini, Chamberlain, Stephanie, Chan, Jeffrey, Chang, Grace, Chaudhry, Dauod, Chebaro, Alexandre, Chen, David, Chetty, Govind, Chia, Zoe, Chiappini, Ambra, Chiarugi, Massimo, Chidambaram, Swathikan, Chiozza, Matteo, Cholewa, Hanna, Chong, Clara, Choolani-Bhojwani, Ekta, Christoforidis, Dimitri, Chui, Karen, Chung, Choyin, Cirillo, Bruno, Citterio, Davide, Clermidi, Pauline, Coccolini, Federico, Colletti, Gaia, Compagnoni, Bruno, Concepción-Martín, Vanesa, Confalonieri, Marco, Connolly, Hannah, Conso, Christel, Conti, Luigi, Cooper, Zara, Cordera, Fernando, Corral, Javier, Costa, Marta, Costanzi, Andrea, Cotsoglou, Christian, Cozza, Valerio, Cuming, Tamzin, Curtis, Miles, Cuschieri, Joseph, D'Agruma, Michele, D'Andrea, Giancarlo, Daliya, Prita, Dare, Oliver, Darko, Ebenezer, Day, Andrew, Dehal, Ahmed, Dehart, Dustin, Delgado-Oliver, Eduardo, Denning, Max, Desai, Anant, Desender, Liesbeth, Dester, Sara, Díaz-García, Alberto, Diaz-Peña, Patricia, Dousset, Bertrand, Doussot, Alexandre, Duchateau, Nicolas, Duff, Sarah, Dunning, Joel, Duque-Mallen, Victoria, Dziakova, Jana, Egan, Bridget, Egan, Richard, El-Ali, Abess, Elfeki, Hossam, Elhadi, Muhammed, Eljareh, Mohammed, Elkady, Ramy, Elkhafeefi, Fatimah, Elmore, Ugo, Elmoslemany, Tarek, Emmerson, Oliver, Enemosah, Ibrahim, English, Camilla, English, William, Escartin, Jorge, Estaire-Gomez, Mercedes, Evans, Luke, Evans, Jessica, Exley, Rebecca, Fabbri, Nicoló, Falco, Giuseppe, Familiari, Pietro, Fancellu, Alessandro, Farik, Shebani, Farrell, Tony, Fehervari, Matyas, Fell, Adam, Fernandez-Camuñas, Angel, Fernández-Marín, Reyes, Fernández-Martínez, María, Ferrara, Francesco, Ferrari, Guglielmo, Ferrero, Simone, Findlay, Laura, Fiore, Marco, Fiori, Enrico, Flatman, Michael, Flindall, Ian, Flor, Blas, Fontana, Tommaso, Ford, Samuel, Ford, David, Forlani, Stefano, Francone, Elisa, Frattaruolo, Colomba, Frio, Federico, Gagliano, Annalisa, Gagliardi, Filippo, Gahunia, Sukhpreet, Gaino, Francesca, Gala, Tanzeela, Galfrascoli, Elisa, Galimberti, Luca, Gallagher, Phoebe, Galleano, Raffaele, Galván-Pérez, Armando, Gammeri, Emanuele, Ganau, Mario, Garcés-García, Raúl, Garulli, Gianluca, Gascon-Ferrer, Isabel, Gattolin, Andrea, Gaujoux, Sebastien, Gentilli, Sergio, Georgiades, Fanourios, Ghanbari, Amir, Ghosh, Dhruv, Giacometti, Marco, Giblin, Anna-Victoria, Gilbert, Catherine, Giménez, Clara, Giorgakis, Emmanouil, Gipponi, Manuel, Glen, Paul, Goatly, Giles, Gobatti, Davide, Godbole, Chintamani, Gohil, Kajal, Gómez, Marcos, Gomez-Rosado, Juan-Carlos, Gonullu, Emre, Gonzalez-Gonzalez, Enrique, Gordini, Luca, Gracia, Isabel, Gracia-Roche, Carlos, Granieri, Stefano, Green, Susanna, Grivon, Manuela, Grove, Thomas, Guaglio, Marcello, Guaitoli, Eleonora, Guglielmi, Alfredo, Guha, Soumya, Gustavino, Claudio, Habeeb, Amir, Hagger, Robert, Hakmi, Hazim, Halkias, Constantine, Hall, Claire, Hampton, Matthew, Handa, Siddhartha, Hansen, Laura, Haq, Iram, Harky, Amer, Harries, Rhiannon, Harrison, Joseph, Hasan, Raashad, Hawari, Mohammad, Hawkin, Paul, Hebblethwaite, Bethany, Henriques, Susana, Heritage, Emily, Hernandez-Juara, Pilar, Herrero-Lopez, Maria, Hervieux, Erik, Heyd, Bruno, Higgs, Simon, Hitchman, Louise, Ho, Beatrice, Hogan, Aisling, Hölzle, Frank, Hossain, Tanvir, Hurt, Libor, Hutchinson, Peter, Iacob, Giulio, Iannone, Immacolata, Ibrahim, Sherif, Iovino, Domenico, Isik, Arda, Jafarova, Sevda, Jamil, Tahir, Jayaraju, Ullas, Jenner, Edward, Jimenez-Higuera, Elisa, Jimeno, Jaime, Jones, Mark, Judkins, Nicholas, Kalavrezos, Nicholas, Kalidindi, Venugopala, Kalkat, Maninder, Kamal, Mona, Kamphues, Carsten, Kang, Chong, Kara, Yasin, Karam, Edward, Karim, Ahmed, Kashora, Florence, Kearney, David, Khajuria, Apoorva, Khan, Umul, Khan, Azam, Khatri, Chetan, Kinnaman, Gabriel, Kinross, James, Kler, Aaron, Klimopoulos, Serafeim, Kocataş, Ali, Kolias, Angelos, Königsrainer, Alfred, Konsten, Joop, Kontovounisios, Christos, Kourdouli, Amar, Krishnan, Emily, Kristinsson, Sverrir, Kruijff, Schelto, Kudsk-Iversen, Søren, Kufeji, Dorothy, Kugler, Nadav, Kulkarni, Rugved, Kurihara, Hayato, Laface, Letizia, Lakkis, Zaher, Lami, Mariam, Landaluce-Olavarria, Aitor, Lapolla, Pierfrancesco, Lawani, Ismail, Lawday, Samuel, Lázaro, André, Lecolle, Katia, Leventoglu, Sezai, Li, Zoe, Liew, Ignatius, Lisi, Giorgio, Lizzi, Vincenzo, Lo, Terence, Lomiento, Daniele, Longhi, Marco, Lostis, Emilie, Lostoridis, Eftychios, Loubani, Mahmoud, Lowy-Benoliel, Alejandro, Lucianetti, Alessandro, Luke, Louis, Lunevicius, Raimundas, Luraghi, Marco, Lye, George, Mabrouk, Islam, Macchi, Alberto, MacDonald, Luisa, Machairas, Nikolaos, Madonini, Marco, Magowan, Drew, Maisonneuve, Emeline, Majkowska, Agata, Majkowski, Lawrence, Mak, Jason, Malabarba, Stefano, Malerba, Michele, Mannan, Syed, Manson, Joanna, Mansuri, Ahmer, Mantoglu, Baris, Manu, Nichola, Maqsood, Afnan, Marano, Alessandra, Marchbank, Adrian, Marcos-Santos, Pablo, Marrano, Enrico, Martin, Janet, Martin, Emmeline, Martin, Guy, Martin-Albo, Lorena, Martín-Román, Lorena, Martinelli, Fabio, Martínez-dePaz, Fernando, Martinez-German, Antonio, Martinez-Pinedo, Carlos, Martins, Ricardo, Marwan, Hisham, Marzi, Federica, Mathieu, Pierre, Matute-Najarro, Maria-Soledad, Maw, Andrew, Mazingi, Dennis, Mazzaferro, Vincenzo, McCanny, Andrew, McKenzie, Katherine, McLarty, Nicola, McPherson, Iain, Medina, Esther, Mediratta, Saniya, Medone, Marzia, Mehra, Gautam, Mele, Simone, Melero-Cortés, Lidia, Mendoza-Moreno, Fernando, Meneghini, Simona, Mercante, Giuseppe, Merdrignac, Aude, Merola, Stephen, Metallidis, Symeon, Michel, Martin, Migliore, Marco, Mihanovic, Jakov, Miller, Douglas, Mingoli, Andrea, Minto, Gary, Mirabella, Antonello, Misra, Nikhil, Mitrasinovic, Stefan, Miu, Victor, Moawad, Nader, Mochet, Sylvie, Modabber, Ali, Mohammad, Adam, Mohan, Midhun, Moliner-Sánchez, Carmen, Mongelli, Francesco, Monteleone, Michela, Montuori, Mauro, Moore, Rachel, Mora-Guzmán, Ismael, Morales, Xavier, Morales, Dieter, Morelli, Luca, Morelli, Lucia, Morgan, Richard, Morris, Chris, Mortini, Pietro, Mosca, Angelo, Motter, Dema, Moug, Susan, Mukherjee, Samrat, Najdy, Manhal, Nakas, Apostolos, Namazov, Ilgar, Naredla, Pradyumna, Nasef, Emmhamed, Nassa, Heeam, Nath, Rahul, Navarro-Sánchez, Antonio, Nazarian, Scarlet, Negri, Giampiero, Nehra, Deepika, Neil-Dwyer, Jason, Neri, Jacopo, Newton, Katy, Nikaj, Herald, Niquen, Milagros, Nobile, Sara, Nogueiro, Jorge, Ntirenganya, Faustin, Nugent, Michael, Núñez, Jordi, Ocaña, Juan, Okechukwu, Valentine, Oliva-Mompean, Fernando, Oliveira, Ana, Ollat, Didier, Onos, Lavinia, Osagie-Clouard, Liza, Osman, Khabab, Ottolina, Jessica, Ourieff, Jared, Outani, Oumaima, Oyewole, Bankole, Ozben, Volkan, Pacheco-Sanchez, David, Padilla-Valverde, David, Pai, Madhava, Paiella, Salvatore, Paisley, Samuel, Palini, Gianmarco, Palmeri, Matteo, Panahi, Pedram, Parente, Alessandro, Parlanti, Daniele, Parmar, Chetan, Pascual, Angela, Patel, Mahul, Pathak, Abhijit, Patil, Sangram, Pattyn, Piet, Peckham-Cooper, Adam, Pedrazzani, Corrado, Pellino, Gianluca, Peluso, Chiara, Pereira, André, Pereira-Neves, António, Perez-Diaz, MD, Pérez-González, Marta, Pérez-Saborido, Baltasar, Perivoliotis, Konstantinos, Perkins, Clare, Peros, Georgios, Perotto, Ornella, Perra, Teresa, Petrone, Patrizio, Phenix, George, Picazo, Sara, Picon-Rodriguez, Rafael, Piloni, Martina, Pingarrón-Martín, Lorena, Pinotti, Enrico, Pisanu, Adolfo, Pizzini, Paolo, Pockney, Peter, Podda, Mauro, Podolsky, Dina, Poggioli, Gilberto, Pompili, Cecilia, Pontari, Michael, Porcu, Alberto, Potter, Ryan, Price, Claire, Pruvot, François-René, Pujol-Muncunill, Roger, Puppo, Andrea, Quante, Markus, Quintana-Villamandos, Begoña, Qureshi, Ali, Radenkovic, Dejan, Rakvin, Ivan, Ramallo-Solís, Irene, Ramcharan, Sean, Ramos, Diego, Ramos-Bonilla, Antonio, Ramzi, Joussi, Rathinam, Sridhar, Rausa, Emanuele, Ravaioli, Matteo, Ravindran, Sharanya, Raymond, Thomas, Razik, Aisha, Redfern, Jennifer, Reguera-Rosal, Julio, Rela, Mariam, Rey-Biel, Juan, Rey-Valcarcel, Cristina, Ribolla, Marta, Richards, Tomos, Richmond, Michael, Righini, Erminio, Rio-Gomez, Javier, Riyat, Harjoat, Rizvi, Sana, Roberts, Keith, Roberts, Matthew, Robertson, Stuart, Robertson, Ronald, Robin-Valle, Alvaro, Rochon, Melissa, Rojo, Mikel, Rolli, Luigi, Romano, Silvio, Ross, Elizabeth, Ross, Howard, Rossborough, Catherine, Rottoli, Matteo, Ruiz-Grande, Fernando, Ruiz-Martin, Irene, Ruiz-Soriano, María, Ruzzenente, Andrea, Ryska, Ondrej, Saez, Carlos, Sagnotta, Andrea, Sahnan, Kapil, Sahni, Arun, Salim, Ali, Sallam, Ibrahim, Salvia, Roberto, Samadov, Elgun, Sammarco, Giuseppe, Sampaio-Alves, Mafalda, Sánchez-Arteaga, Alejandro, Sanchez-Fuentes, Maria-Nieves, Sanchez-Pelaez, Daniel, Sanchez-Perez, Coral, Sanchez-Rubio, Maria, Sancho-Muriel, Jorge, Sanders, Julie, Santero-Ramirez, Maria-Pilar, Santora, Thomas, Santoro, Antonio, Santos, Irene, Santos-Sousa, Hugo, Sapienza, Paolo, Sartarelli, Lodovico, Sarveswaran, Janahan, Sasia, Diego, Saudemont, Alain, Saudi-Moro, Sef, Saxena, Shobhit, Saxena, Dolly, Sayasneh, Ahmad, Scalabre, Aurelien, Schache, Andrew, Schiavina, Riccardo, Schineis, Christian, Schreckenbach, Teresa, Scorza, Antonella, Scott, Lucy, Seegert, Sara, Seguin-Givelet, Agathe, Senent-Boza, Ana, Seymour, Keith, Shabana, Amanda, Shah, Karishma, Shah, Jigar, Shah, Preena, Shah, Sujay, Shakir, Taner, Shalaby, Mostafa, Shankar, Sushma, Shaw, Richard, Shehata, Sameh, Shenfine, Amy, Sheridan, Kelda, Sherief, Ahmed, Sherief, Mohamed, Sherif, Mohamed, Shinkwin, Michael, Shu, Sebastian, Siaw-Acheampong, Kwabena, Sileri, Pierpaolo, Singh, Abhinav, Singh, Shailendra, Sinha, Sanjay, Sinha, Deepti, Siragusa, Leandro, Sivaprakasam, Rajesh, Sivayoganathan, Sriharan, Smillie, Robert, Smith, Claire, Smith, Andrew, Smith, Christopher, Sochorova, Dana, Soggiu, Fiammetta, Sohrabi, Catrin, Solari, Francesca, Solli, Piergiorgio, Soreide, Kjetil, Spinelli, Antonino, Spoletini, Domenico, Spriano, Giuseppe, Sravanam, Sanskrithi, Ssentongo, Paddy, Stanger, Sophie, Stavroulias, Dionisios, Steel, Ben, Stella, Marco, Stewart, Robbie, Stringer, Sally, Sulen, Nina, Sundar, Sudha, Sundhu, Matthew, Suri, Avni, Syed, Arooj, Szatmary, Peter, Tabiri, Stephen, Tadross, Daniel, Taglietti, Lucio, Tansey, Rosamond, Tartaglia, Dario, Tawheed, Ahmed, Tayeh, Salim, Teles, Tobias, Testa, Valentina, Tewari, Nilanjana, Thoenissen, Philipp, Thomas, Kane, Thomin, Anne, Thrush, Jessica, Tierney, Sean, Tiwari, Abhinav, Toh, Simon, Toledo, Enrique, Tonini, Valeria, Torkington, Jared, Torquati, Alfonso, Torzilli, Guido, Totty, Joshua, Tourountzi, Paraskevi, Tousidonis, Manuel, Townend, Philip, Townsend, Catherine, Trompeter, Alex, Trotta, Francesco, Truant, Stéphanie, Trujillo-Díaz, Jeancarlos, Tsoulfas, Georgios, Turco, Celia, Turrado-Rodriguez, Victor, Turri, Giulia, Tustin, Harry, Tyler, Jayne, Tzedakis, Stylianos, Tzovaras, George, Uittenbogaart, Martine, Ullah, Ramzan, Urban, Shane, Urbani, Alessia, Usai, Antonella, Vaccarella, Gianpaolo, Valdes-Hernandez, Javier, Valsecchi, Luca, Vashisht, Rajiv, Vázquez-Fernández, Andrea, Venn, Mary, Vera-Mansilla, Cristina, Vergari, Roberto, Vescio, Giuseppina, Vidya, Raghavan, Vieira, Paula, Vijay, Vardhini, Vimalachandran, Dale, Violante, Tommaso, Volpe, Anita, Vovola, Fernanda, Vulliamy, Paul, Wall, Rosemary, Wallwork, Kate, Ward, Alex, Warwick, David, Waseem, Saima, Weaver, Helen, Wells, Fiona, Wen, Jiaxin, West, Raha, Whitehall, Emma, Wild, Laura, Wilkins, Alex, Williams, Gethin, Williams, Matthew, Winnand, Philipp, Wong, Ken, Worku, Dawit, Wright, Naomi, Yalamanchili, Seema, Yershov, Danylo, Yildiz, Alp, Young, Richard, Yurttas, Can, Zadegan, Frederic, Zafar, Noman, Zakaria, Rasheed, Zambon, Martina, Zanini, Nicola, Zarate, Alba, Zerbib, Philippe, Zizzo, Maurizio, Zmora, Oded, Zonta, Sandro, van Berge Henegouwen, Mark I, van der Plas, Willemijn Y, Ali, Inthekab Ali Mohamed, Bakri, Nur Amalina Che, Bauset, Juan Carlos Catalá, Abou Chaar, Mohamad K, Marino Cosentino, Luigi MP, Gómez Díaz, Carlos J, Garcia Galocha, Jose L, de Gheldere, Charles A, Ataíde Gomes, Gustavo Mendonça, Beltrán de Heredia, Juan, Blazer III, Dan G, Nugent III, William C, Ali karar, Ali A, Borja De Lacy, F, Blas Laina, Juan luis, Shane Lester, Madan Jha, Liyanage, Aloka S D, Al Maadany, Faraj S, De Marchi, Joshua A, Ramos-De la Medina, Antonio, Mithany, Reda H M, Sanchez del Pueblo, Cristina, van Ramshorst, Gabrielle H, De Salas, Maria Marqueta, De Souza, Anthony C, Dolores Del Toro, M, Archer, James E, Odeh, Abdulrahman, Erridge, Simon, Salem, Hosni Khairy, Jones, Gareth P, Gardner, Anne, Tripathi, Shiva S, Gregg, Anne, Jeganathan, Reuben, Siddique, Muhammad Harris, Lombardi, Celestino P, Martin, Benjamin, Leo, Cosimo Alex, Dass, Debashis, Di Franco, Gregorio, Jiao, Long R, Mari, Giulio Maria, Capitan-Morales, Luis-Cristobal, Connelly, Tara M, Alanbuki, Ammar, De Virgilio, Armando, Schilling, Clare, San Miguel Mendez, Carlos, Kulkarni, Gauri, Nizami, Kulsoom, Walsh, Stewart, Dean, Harry, Ruiz-Marin, Miguel, Houston, Rory, Trompetto, Mario, Chrastek, David, Kouritas, Vasileios, Cannoletta, Maria, Rosato, Francesco, Kaushal, Manish V, Costa, Paulo Matos, Elkadi, Hannah H, Johnstone, Jack R, Irvine, Esmee, Alvarez, Maria R, Corbellini, Carlo, Venkatesan, Gowtham S, Mateo-Sierra, Olga, Martínez-Pérez, Carolina, Serrano González, Javier, Hernández Bartolomé, Miguel Ángel, Díaz Pérez, David, Gutiérrez Samaniego, María, Galindo Jara, Pablo, Sharma, Neil, Smart, Neil J, and Keller, Deborah S
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- 2020
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9. 340.12: The Beneficial Effect of Renal Transplantation on the Immune Phenotype of T Lymphocytes
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Vagiotas, Lampros, Fylaktou, Asimina, Kassimatis, Efstratios, Xochelli, Aliki, Sampani, Erasmia, Nikolaidou, Vasiliki, Daoudaki, Maria, Tsoulfas, George, Papagianni, Aikaterini, and Stangou, Maria
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- 2022
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10. #621 Omental, nodal and bowel invasion rate in advanced-stage ovarian cancer patients: prospective observational study
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Margioula-Siarkou, Chrysoula, primary, Petousis, Stamatios, additional, Almperis, Aristarchos, additional, Almperi, Emmanuela-Aliki, additional, Kofinas, Athanasios, additional, Tsoulfas, George, additional, Papanikolaou, Alexios, additional, and Dinas, Konstantinos, additional
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- 2023
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11. International Scholarship Programs of the American College of Surgeons: Expansion of the Global Surgical Network
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Nigri, Giuseppe, Early, Kate, Tsoulfas, George, Ferreres, Alberto, Ferrone, Cristina R., Schulick, Richard, Al-Refaie, Waddah B., Turner, Patricia L., and Velmahos, George
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- 2018
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12. Cardiovascular disease/stroke risk stratification in deep learning framework: a review
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Bhagawati, Mrinalini, primary, Paul, Sudip, additional, Agarwal, Sushant, additional, Protogeron, Athanasios, additional, Sfikakis, Petros P., additional, Kitas, George D., additional, Khanna, Narendra N., additional, Ruzsa, Zoltan, additional, Sharma, Aditya M., additional, Tomazu, Omerzu, additional, Turk, Monika, additional, Faa, Gavino, additional, Tsoulfas, George, additional, Laird, John R., additional, Rathore, Vijay, additional, Johri, Amer M., additional, Viskovic, Klaudija, additional, Kalra, Manudeep, additional, Balestrieri, Antonella, additional, Nicolaides, Andrew, additional, Singh, Inder M., additional, Chaturvedi, Seemant, additional, Paraskevas, Kosmas I., additional, Fouda, Mostafa M., additional, Saba, Luca, additional, and Suri, Jasjit S., additional
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- 2023
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13. Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework
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Dubey, Arun Kumar, Chabert, Gian Luca, Carriero, Alessandro, Pasche, Alessio, Danna, Pietro S. C., Agarwal, Sushant, Mohanty, Lopamudra, Nillmani, Lopamudra, Sharma, Neeraj, Yadav, Sarita, Jain, Achin, Kumar, Ashish, Kalra, Mannudeep K., Sobel, David W., Laird, John R., Singh, Inder M., Singh, Narpinder, Tsoulfas, George, Fouda, Mostafa M., Alizad, Azra, Kitas, George D., Khanna, Narendra N., Viskovic, Klaudija, Kukuljan, Melita, Al-Maini, Mustafa, El-Baz, Ayman, Saba, Luca, and Suri, Jasjit S.
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ResNet–UNet ,transfer learning ,ensemble deep learning ,BIOMEDICINA I ZDRAVSTVO. Kliničke medicinske znanosti. Radiologija ,control ,BIOMEDICINE AND HEALTHCARE. Clinical Medical Sciences. Radiology ,COVID ,unseen - Abstract
Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet–UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL’s. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts—Croatia (80 COVID) and Italy (72 COVID and 30 controls)—leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.
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- 2023
14. Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment
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Khanna, Narendra N., primary, Maindarkar, Mahesh A., additional, Viswanathan, Vijay, additional, Fernandes, Jose Fernandes E, additional, Paul, Sudip, additional, Bhagawati, Mrinalini, additional, Ahluwalia, Puneet, additional, Ruzsa, Zoltan, additional, Sharma, Aditya, additional, Kolluri, Raghu, additional, Singh, Inder M., additional, Laird, John R., additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Saba, Luca, additional, Agarwal, Vikas, additional, Sharma, Aman, additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Rathore, Vijay, additional, Naidu, Subbaram, additional, Liblik, Kiera, additional, Johri, Amer M., additional, Turk, Monika, additional, Mohanty, Lopamudra, additional, Sobel, David W., additional, Miner, Martin, additional, Viskovic, Klaudija, additional, Tsoulfas, George, additional, Protogerou, Athanasios D., additional, Kitas, George D., additional, Fouda, Mostafa M., additional, Chaturvedi, Seemant, additional, Kalra, Mannudeep K., additional, and Suri, Jasjit S., additional
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- 2022
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15. Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study
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Khanna, Narendra N., primary, Maindarkar, Mahesh A., additional, Viswanathan, Vijay, additional, Puvvula, Anudeep, additional, Paul, Sudip, additional, Bhagawati, Mrinalini, additional, Ahluwalia, Puneet, additional, Ruzsa, Zoltan, additional, Sharma, Aditya, additional, Kolluri, Raghu, additional, Krishnan, Padukone R., additional, Singh, Inder M., additional, Laird, John R., additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Dhanjil, Surinder K., additional, Saba, Luca, additional, Balestrieri, Antonella, additional, Faa, Gavino, additional, Paraskevas, Kosmas I., additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Sharma, Aman, additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Nicolaides, Andrew, additional, Rathore, Vijay, additional, Naidu, Subbaram, additional, Liblik, Kiera, additional, Johri, Amer M., additional, Turk, Monika, additional, Sobel, David W., additional, Miner, Martin, additional, Viskovic, Klaudija, additional, Tsoulfas, George, additional, Protogerou, Athanasios D., additional, Mavrogeni, Sophie, additional, Kitas, George D., additional, Fouda, Mostafa M., additional, Kalra, Mannudeep K., additional, and Suri, Jasjit S., additional
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- 2022
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16. Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report
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Khanna, Narendra N., primary, Maindarkar, Mahesh, additional, Puvvula, Anudeep, additional, Paul, Sudip, additional, Bhagawati, Mrinalini, additional, Ahluwalia, Puneet, additional, Ruzsa, Zoltan, additional, Sharma, Aditya, additional, Munjral, Smiksha, additional, Kolluri, Raghu, additional, Krishnan, Padukone R., additional, Singh, Inder M., additional, Laird, John R., additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Dhanjil, Surinder K., additional, Saba, Luca, additional, Balestrieri, Antonella, additional, Faa, Gavino, additional, Paraskevas, Kosmas I., additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Sharma, Aman, additional, Teji, Jagjit, additional, Al-Maini, Mustafa, additional, Nicolaides, Andrew, additional, Rathore, Vijay, additional, Naidu, Subbaram, additional, Liblik, Kiera, additional, Johri, Amer M., additional, Turk, Monika, additional, Sobel, David W., additional, Pareek, Gyan, additional, Miner, Martin, additional, Viskovic, Klaudija, additional, Tsoulfas, George, additional, Protogerou, Athanasios D., additional, Mavrogeni, Sophie, additional, Kitas, George D., additional, Fouda, Mostafa M., additional, Kalra, Manudeep K., additional, and Suri, Jasjit S., additional
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- 2022
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17. Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review
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Suri, Jasjit S., primary, Maindarkar, Mahesh A., additional, Paul, Sudip, additional, Ahluwalia, Puneet, additional, Bhagawati, Mrinalini, additional, Saba, Luca, additional, Faa, Gavino, additional, Saxena, Sanjay, additional, Singh, Inder M., additional, Chadha, Paramjit S., additional, Turk, Monika, additional, Johri, Amer, additional, Khanna, Narendra N., additional, Viskovic, Klaudija, additional, Mavrogeni, Sofia, additional, Laird, John R., additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanase D., additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Kolluri, Raghu, additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Sockalingam, Meyypan, additional, Saxena, Ajit, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Padukode R., additional, Omerzu, Tomaz, additional, Naidu, Subbaram, additional, Nicolaides, Andrew, additional, Paraskevas, Kosmas I., additional, Kalra, Mannudeep, additional, Ruzsa, Zoltán, additional, and Fouda, Mostafa M., additional
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- 2022
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18. COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans
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Suri, Jasjit S., primary, Agarwal, Sushant, additional, Chabert, Gian Luca, additional, Carriero, Alessandro, additional, Paschè, Alessio, additional, Danna, Pietro S. C., additional, Saba, Luca, additional, Mehmedović, Armin, additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios D., additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Fouda, Mostafa M., additional, Naidu, Subbaram, additional, Viskovic, Klaudija, additional, and Kalra, Mannudeep K., additional
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- 2022
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19. COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans
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Suri, Jasjit S., primary, Agarwal, Sushant, additional, Chabert, Gian Luca, additional, Carriero, Alessandro, additional, Paschè, Alessio, additional, Danna, Pietro S. C., additional, Saba, Luca, additional, Mehmedović, Armin, additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios D., additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Fouda, Mostafa M., additional, Naidu, Subbaram, additional, Viskovic, Klaudija, additional, and Kalra, Manudeep K., additional
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- 2022
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20. Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review
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Munjral, Smiksha, primary, Maindarkar, Mahesh, additional, Ahluwalia, Puneet, additional, Puvvula, Anudeep, additional, Jamthikar, Ankush, additional, Jujaray, Tanay, additional, Suri, Neha, additional, Paul, Sudip, additional, Pathak, Rajesh, additional, Saba, Luca, additional, Chalakkal, Renoh Johnson, additional, Gupta, Suneet, additional, Faa, Gavino, additional, Singh, Inder M., additional, Chadha, Paramjit S., additional, Turk, Monika, additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Viskovic, Klaudija, additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios, additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Kolluri, Raghu, additional, Teji, Jagjit, additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Sockalingam, Meyypan, additional, Saxena, Ajit, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Viswanathan, Vijay, additional, Krishnan, Padukode R., additional, Omerzu, Tomaz, additional, Naidu, Subbaram, additional, Nicolaides, Andrew, additional, Fouda, Mostafa M., additional, and Suri, Jasjit S., additional
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- 2022
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21. Editorial: Immersive Media in Connected Health
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Antoniou, Panagiotis E., Economou, Daphne, Athanasiou, Alkinoos, and Tsoulfas, George
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surgery ,telepresence ,Editorial ,Digital Health ,3D printing ,medical education ,mixed reality - Published
- 2021
22. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts
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Suri, Jasjit S., primary, Agarwal, Sushant, additional, Carriero, Alessandro, additional, Paschè, Alessio, additional, Danna, Pietro S. C., additional, Columbu, Marta, additional, Saba, Luca, additional, Viskovic, Klaudija, additional, Mehmedović, Armin, additional, Agarwal, Samriddhi, additional, Gupta, Lakshya, additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios, additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Gupta, Archna, additional, Naidu, Subbaram, additional, Paraskevas, Kosmas I., additional, and Kalra, Mannudeep K., additional
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- 2021
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23. Correction to: International Scholarship Programs of the American College of Surgeons: Expansion of the Global Surgical Network
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Nigri, Giuseppe, Early, Kate, Tsoulfas, George, Ferreres, Alberto, Ferrone, Cristina R., Schulick, Richard, Al-Refaie, Waddah B., Turner, Patricia L., and Velmahos, George
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- 2018
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24. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography
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Suri, Jasjit, primary, Agarwal, Sushant, additional, Elavarthi, Pranav, additional, Pathak, Rajesh, additional, Ketireddy, Vedmanvitha, additional, Columbu, Marta, additional, Saba, Luca, additional, Gupta, Suneet, additional, Faa, Gavino, additional, Singh, Inder, additional, Turk, Monika, additional, Chadha, Paramjit, additional, Johri, Amer, additional, Khanna, Narendra, additional, Viskovic, Klaudija, additional, Mavrogeni, Sophie, additional, Laird, John, additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David, additional, Balestrieri, Antonella, additional, Sfikakis, Petros, additional, Tsoulfas, George, additional, Protogerou, Athanasios, additional, Misra, Durga, additional, Agarwal, Vikas, additional, Kitas, George, additional, Teji, Jagjit, additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder, additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode, additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Gupta, Archna, additional, Naidu, Subbaram, additional, and Kalra, Mannudeep, additional
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- 2021
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25. In vivo Simulation-Based Learning for Undergraduate Medical Students: Teaching and Assessment [Response to Letter]
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Sideris, Michail, primary, Hanrahan, John Gerrard, additional, Nicolaides, Marios, additional, Jagiello, Jade, additional, Rallis, Kathrine S, additional, Emin, Elif, additional, Theodorou, Efthymia, additional, Mallick, Rebecca, additional, Odejinmi, Funlayo, additional, Lymperopoulos, Nikolaos, additional, Papalois, Apostolos, additional, and Tsoulfas, George, additional
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- 2021
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26. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography
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Suri, Jasjit S. Agarwal, Sushant Elavarthi, Pranav Pathak, Rajesh Ketireddy, Vedmanvitha Columbu, Marta Saba, Luca and Gupta, Suneet K. Faa, Gavino Singh, Inder M. Turk, Monika and Chadha, Paramjit S. Johri, Amer M. Khanna, Narendra N. and Viskovic, Klaudija Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Sobel, David W. Balestrieri, Antonella and Sfikakis, Petros P. Tsoulfas, George Protogerou, Athanasios and Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. and Teji, Jagjit S. Al-Maini, Mustafa Dhanjil, Surinder K. and Nicolaides, Andrew Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra Krishnan, Pudukode R. Ferenc, Nagy and Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Kalra, Mannudeep K.
- Abstract
Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.
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- 2021
27. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts
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Suri, Jasjit S. Agarwal, Sushant Carriero, Alessandro and Pasche, Alessio Danna, Pietro S. C. Columbu, Marta Saba, Luca Viskovic, Klaudija Mehmedovic, Armin Agarwal, Samriddhi and Gupta, Lakshya Faa, Gavino Singh, Inder M. Turk, Monika and Chadha, Paramjit S. Johri, Amer M. Khanna, Narendra N. and Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin and Sobel, David W. Balestrieri, Antonella Sfikakis, Petros P. and Tsoulfas, George Protogerou, Athanasios Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. Teji, Jagjit S. and Al-Maini, Mustafa Dhanjil, Surinder K. Nicolaides, Andrew and Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra and Krishnan, Pudukode R. Nagy, Ferenc Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Paraskevas, Kosmas I. Kalra, Mannudeep K.
- Abstract
(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020-2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPoint(TM) Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland-Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of
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- 2021
28. COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models
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Suri, Jasjit S. Agarwal, Sushant Pathak, Rajesh Ketireddy, Vedmanvitha Columbu, Marta Saba, Luca Gupta, Suneet K. and Faa, Gavino Singh, Inder M. Turk, Monika Chadha, Paramjit S. and Johri, Amer M. Khanna, Narendra N. Viskovic, Klaudija and Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin and Sobel, David W. Balestrieri, Antonella Sfikakis, Petros P. and Tsoulfas, George Protogerou, Athanasios Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. Teji, Jagjit S. and Al-Maini, Mustafa Dhanjil, Surinder K. Nicolaides, Andrew and Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra and Krishnan, Pudukode R. Frence, Nagy Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Kalra, Mannudeep
- Abstract
Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint (TM), Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of similar to 0.96, similar to 0.97, similar to 0.98, and similar to 0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in
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- 2021
29. Long-term Outcome of a Cuffed Expanded PTFE Graft for Hemodialysis Vascular Access
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Tsoulfas, George, Hertl, Martin, Ko, Dicken S. C., Elias, Nahel, Delmonico, Francis L., Romano, Linda, Fernandes, Isabel, Schoenfeld, David, and Kawai, Tatsuo
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- 2008
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30. COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models
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Suri, Jasjit S., primary, Agarwal, Sushant, additional, Pathak, Rajesh, additional, Ketireddy, Vedmanvitha, additional, Columbu, Marta, additional, Saba, Luca, additional, Gupta, Suneet K., additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Viskovic, Klaudija, additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios, additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Frence, Nagy, additional, Ruzsa, Zoltan, additional, Gupta, Archna, additional, Naidu, Subbaram, additional, and Kalra, Mannudeep, additional
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- 2021
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31. In vivo Simulation-Based Learning for Undergraduate Medical Students: Teaching and Assessment [Response to Letter]
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Sideris,Michail, Hanrahan,John Gerrard, Nicolaides,Marios, Jagiello,Jade, Rallis,Kathrine S, Emin,Elif, Theodorou,Efthymia, Mallick,Rebecca, Odejinmi,Funlayo, Lymperopoulos,Nikolaos, Papalois,Apostolos, Tsoulfas,George, Sideris,Michail, Hanrahan,John Gerrard, Nicolaides,Marios, Jagiello,Jade, Rallis,Kathrine S, Emin,Elif, Theodorou,Efthymia, Mallick,Rebecca, Odejinmi,Funlayo, Lymperopoulos,Nikolaos, Papalois,Apostolos, and Tsoulfas,George
- Abstract
Michail Sideris,1,* John Gerrard Hanrahan,2,* Marios Nicolaides,3 Jade Jagiello,4 Kathrine S Rallis,3 Elif Emin,1 Efthymia Theodorou,3 Rebecca Mallick,5 Funlayo Odejinmi,6 Nikolaos Lymperopoulos,7 Apostolos Papalois,8 George Tsoulfas9 1Women’s Health Research Unit, Queen Mary University of London, London, UK; 2Addenbrooke’s Hospital, Cambridge University Hospitals, Cambridge, UK; 3Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK; 4University College, London Hospital, London, UK; 5Princess Royal Hospital, Brighton and Sussex University Hospitals NHS Trust, Haywards Heath, UK; 6Whipps Cross University Hospital, Barts Health NHS Trust, London, UK; 7Guy’s and St Thomas Hospitals NHS Foundation Trust, London, UK; 8Special Unit for Biomedical Research and Education, Aristotle University of Thessaloniki, Thessaloniki, Greece; 9Department of Surgery, Aristotle University of Thessaloniki, Thessaloniki, Greece*These authors contributed equally to this workCorrespondence: Michail SiderisWomen’s Health Research Unit, Queen Mary University of London, Yvonne Carter Building, 58 Turner Street, London, E1 2AB, UKEmail m.sideris@qmul.ac.uk View the original paper by Dr Sideris and colleagues This is in response to the Letter to the Editor
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- 2021
32. In vivo Simulation-Based Learning for Undergraduate Medical Students: Teaching and Assessment
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Sideris,Michail, Nicolaides,Marios, Jagiello,Jade, Rallis,Kathrine S, Emin,Elif, Theodorou,Efthymia, Hanrahan,John Gerrard, Mallick,Rebecca, Odejinmi,Funlayo, Lymperopoulos,Nikolaos, Papalois,Apostolos, Tsoulfas,George, Sideris,Michail, Nicolaides,Marios, Jagiello,Jade, Rallis,Kathrine S, Emin,Elif, Theodorou,Efthymia, Hanrahan,John Gerrard, Mallick,Rebecca, Odejinmi,Funlayo, Lymperopoulos,Nikolaos, Papalois,Apostolos, and Tsoulfas,George
- Abstract
Michail Sideris, 1,* Marios Nicolaides, 2,* Jade Jagiello, 3 Kathrine S Rallis, 2 Elif Emin, 1 Efthymia Theodorou, 2 John Gerrard Hanrahan, 4 Rebecca Mallick, 5 Funlayo Odejinmi, 6 Nikolaos Lymperopoulos, 7 Apostolos Papalois, 8 George Tsoulfas 9 1Women’s Health Research Unit, Queen Mary University of London, London, UK; 2Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK; 3University College London Hospital, London, UK; 4Addenbrooke’s Hospital, Cambridge University Hospitals, Cambridge, UK; 5Princess Royal Hospital, Brighton and Sussex University Hospitals NHS Trust, Haywards Heath, UK; 6Whipps Cross University Hospital, Barts Health NHS Trust, London, UK; 7Guy’s and St Thomas Hospitals NHS Foundation Trust, London, UK; 8Special Unit for Biomedical Research and Education, Aristotle University of Thessaloniki, Thessaloniki, Greece; 9Department of Surgery, Aristotle University of Thessaloniki, Thessaloniki, Greece*These authors contributed equally to this workCorrespondence: Michail SiderisWomen’s Health Research Unit, Queen Mary University of London, Yvonne Carter Building, 58 Turner Street, London, E1 2AB, UKEmail m.sideris@qmul.ac.ukAbstract: An increasing emphasis on simulation has become evident in the last three decades following fundamental shifts in the medical profession. Simulation-based learning (SBL) is a wide term that encompasses several means for imitating a skill, attitude, or procedure to train personnel in a safe and adaptive environment. A classic example has been the use of live animal tissue, named in vivo SBL. We aimed to review all published evidence on in vivo SBL for undergraduate medical students; this includes both teaching concepts as well as focused assessment of students on those concepts. We performed a systematic review of published evidence on MEDLINE. We also incorporated evidence from a series of systematic reviews (eviCORE) focused on undergradu
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- 2021
33. Hydrodynamic Plasmid DNA Gene Therapy Model in Liver Transplantation
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Tsoulfas, George, Takahashi, Yoshihito, Liu, Dexi, Yagnik, Gautam, Wu, Tong, Murase, Noriko, and Geller, David A.
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- 2006
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34. Role of NF-[kappa]B on liver cold ischemia-reperfusion injury
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Takahashi, Yoshihito, Ganster, Raymond W., Gambotto, Andrea, Shao, Lifang, Kaizu, Takashi, Wu, Tong, Yagnik, Gautam P., Nakao, Atsunori, Tsoulfas, George, Ishikawa, Takashi, Okuda, Toyokazu, Geller, David A., and Murase, Noriko
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Apoptosis -- Physiological aspects ,Biological sciences - Abstract
The role of NF-[kappa]B, the rapid-response transcription factor for multiple genes, in cold ischemia-reperfusion (I/R) injury was examined after syngeneic transplantation of liver grafts. Lewis rat recipients were killed 1-48 h after reperfusion of three different liver grafts: 1) uninfected control, 2) infected ex vivo with control adenoviral vector (AdEGFP), and 3) infected ex vivo with AdI[kappa]B. In uninfected control livers, NF-[kappa]B was activated biphasically at 1-3 and 12 h after reperfusion with aspartate transaminase (AST) levels of 4,244 [+ or -] 691 IU/1. The first peak of NF-[kappa]B activation associated with an increase of mRNA for TNF-[alpha], IL-[beta], and IL-10. AdEGFP transfection resulted in similar outcomes. Interestingly, AdI[kappa]B-transfected liver grafts suffered more severe I/R injury (AST > 9,000 IU/1). Transfected I[kappa]B was detected in transplanted livers as early as 6 h, and this correlated with the abrogation of the second, but not the first, peak of NF-[kappa]B activation at 12-48 h and increased apoptosis. Thus inhibition of the second wave of NF-[kappa]B activation in I[kappa]B-transfected livers resulted in an increase of liver injury, suggesting that NF-[kappa]B may have a dual role during liver I/R injury. liver transplantation; preservation injuries; I[kappa]B; adenoviral vector
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- 2002
35. The critical pathway for deceased donation: reportable uniformity in the approach to deceased donation
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Domínguez-Gil, Beatriz, Delmonico, Francis L., Shaheen, Faissal A. M., Matesanz, Rafael, OʼConnor, Kevin, Minina, Marina, Muller, Elmi, Young, Kimberly, Manyalich, Marti, Chapman, Jeremy, Kirste, Günter, Al-Mousawi, Mustafa, Coene, Leen, García, Valter Duro, Gautier, Serguei, Hasegawa, Tomonori, Jha, Vivekanand, Kwek, Tong Kiat, Chen, Zhonghua Klaus, Loty, Bernard, Costa, Alessandro Nanni, Nathan, Howard M., Ploeg, Rutger, Reznik, Oleg, Rosendale, John D., Tibell, Annika, Tsoulfas, George, Vathsala, Anantharaman, and Noël, Luc
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- 2011
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36. High mortality in orthotopic liver transplant recipients who require hemodialysis
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Zand, Martin S., Orloff, Mark S., Abt, Peter, Patel, Siddharth, Tsoulfas, George, Kashyap, Randeep, Jain, Ashok, Safadjou, Saman, and Bozorgzadeh, Adel
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- 2011
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37. Evaluating trauma care, outcomes and costs in a system in crisis: the necessity of a Greek National Trauma Database
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Prionas, Apostolos, primary, Tsoulfas, George, additional, Tooulias, Andreas, additional, Papakoulas, Apostolos, additional, Piachas, Athanasios, additional, and Papadopoulos, Vasileios, additional
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- 2020
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38. Laparoscopic surgery for the prevention, palliation, and cure of gastrointestinal malignancies
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Torres, Jose E., Tsoulfas, George, Hamdy, Kareem, and Scott-Conner, Carol E.H.
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- 2005
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39. Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence
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Suri, Jasjit S. Puvvula, Anudeep Majhail, Misha Biswas, Mainak Jamthikar, Ankush D. Saba, Luca Faa, Gavino and Singh, Inder M. Oberleitner, Ronald Turk, Monika Srivastava, Saurabh Chadha, Paramjit S. Suri, Harman S. Johri, Amer M. and Nambi, Vijay Sanches, J. Miguel Khanna, Narendra N. and Viskovic, Klaudija Mavrogeni, Sophie Laird, John R. Bit, Arindam Pareek, Gyan Miner, Martin Balestrien, Antonella and Sfikakis, Petros P. Tsoulfas, George Protogerou, Athanasios and Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. and Kolluri, Raghu Teji, Jagjit Porcu, Michele Al-Maini, Mustafa and Agbakoba, Ann Sockalingam, Meyypan Sexena, Ajit and Nicolaides, Andrew Sharma, Aditya Rathore, Vijay and Viswanathan, Vijay Naidu, Subbaram Bhatt, Deepak L.
- Abstract
Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic “cognitive” functions that we associate with our mind, such as “learning” and “solving problem”. New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.
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- 2020
40. The surgical management of parasitic diseases
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Tsoulfas, George, editor, Hoballah, Jamal J., editor, Velmahos, George C., editor, Ho, Yik-Hong, editor, Tsoulfas, George, editor, Hoballah, Jamal J., editor, Velmahos, George C., editor, and Ho, Yik-Hong, editor
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- 2020
41. NF-κB in transplantation: friend or foe?
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Tsoulfas, George and Geller, David A.
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- 2001
42. Protective Role of the L-Arginine-Nitric Oxide Synthase Pathway on Preservation Injury after Rat Liver Transplantation
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Geller, David A., Chia, Stanley H., Takahashi, Yoshihito, Yagnik, Gautam P., Tsoulfas, George, and Murase, Noriko
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- 2001
43. Blockade of the L-arginine/NO synthase pathway worsens hepatic apoptosis and liver transplant preservation injury
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Yagnik, Gautam P., Takahashi, Yoshihito, Tsoulfas, George, Reid, Kaye, Murase, Noriko, and Geller, David A.
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- 2002
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44. Correction to: International Scholarship Programs of the American College of Surgeons: Expansion of the Global Surgical Network
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Nigri, Giuseppe, primary, Early, Kate, additional, Tsoulfas, George, additional, Ferreres, Alberto, additional, Ferrone, Cristina R., additional, Schulick, Richard, additional, Al-Refaie, Waddah B., additional, Turner, Patricia L., additional, and Velmahos, George, additional
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- 2017
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45. International Scholarship Programs of the American College of Surgeons: Expansion of the Global Surgical Network
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Nigri, Giuseppe, primary, Early, Kate, additional, Tsoulfas, George, additional, Ferreres, Alberto, additional, Ferrone, Cristina R., additional, Schulick, Richard, additional, Al-Refaie, Waddah B., additional, Turner, Patricia L., additional, and Velmahos, George, additional
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- 2017
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46. QS364. Longterm Outcome of a Cuffed Expanded PTFE Graft for Hemodialysis Vascular Access
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Tsoulfas, George, primary, Hertl, Martin, additional, Ko, Dicken S.C., additional, Elias, Nahel, additional, Delmonico, Francis L., additional, Cosimi, Benedict A., additional, Schoenfeld, David, additional, and Kawai, Tatsuo, additional
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- 2008
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47. Disparate patient survival among UNOS regions after liver transplantation for hepatocellular carcinoma
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Patel, Siddharth A., primary, Freeman, Richard, additional, Orloff, Mark, additional, Tsoulfas, George, additional, Kashyap, Randeep, additional, Jain, Ashok, additional, Bozorgzadeh, Adel, additional, and Abt, Peter, additional
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- 2007
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48. Pediatric Liver and Kidney Transplantation With Allografts From DCD Donors: A Review of UNOS Data
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Abt, Peter, primary, Kashyap, Randeep, additional, Orloff, Mark, additional, Jain, Ashok, additional, Tsoulfas, George, additional, Bozorgzadeh, Adel, additional, and Olthoff, Kim, additional
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- 2006
- Full Text
- View/download PDF
49. Activation of the lipopolysaccharide signaling pathway in hepatic transplantation preservation injury12
- Author
-
Tsoulfas, George, primary, Takahashi, Yoshihito, additional, Ganster, Raymond W., additional, Yagnik, Gautam, additional, Guo, Zhong, additional, Fung, John J., additional, Murase, Noriko, additional, and Geller, David A., additional
- Published
- 2002
- Full Text
- View/download PDF
50. Former Prisoners’ Views on Mandatory HIV Testing During Incarceration
- Author
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Ramratnam, Bharat, primary, Rich, Josiah D., additional, Parikh, Ashish, additional, Tsoulfas, George, additional, Vigilante, Kevin C., additional, and Flanigan, Timothy P., additional
- Published
- 1997
- Full Text
- View/download PDF
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