| Predicting the risk of developing diabetic retinopathy using deep learning A Bora, S Balasubramanian, B Babenko, S Virmani, S Venugopalan, ... The Lancet Digital Health 3 (1), e10-e19, 2021 | 163 | 2021 |
| Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy R Raman, S Srinivasan, S Virmani, S Sivaprasad, C Rao, R Rajalakshmi Eye 33 (1), 97-109, 2019 | 151 | 2019 |
| Towards generalist biomedical AI T Tu, S Azizi, D Driess, M Schaekermann, M Amin, PC Chang, A Carroll, ... NEJM AI 1 (3), AIoa2300138, 2024 | 142 | 2024 |
| Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study P Ruamviboonsuk, R Tiwari, R Sayres, V Nganthavee, K Hemarat, ... The Lancet Digital Health 4 (4), e235-e244, 2022 | 109 | 2022 |
| Dynamic perfusion CT assessment of the blood-brain barrier permeability: first pass versus delayed acquisition JW Dankbaar, J Hom, T Schneider, SC Cheng, BC Lau, I Van Der Schaaf, ... American Journal of Neuroradiology 29 (9), 1671-1676, 2008 | 76 | 2008 |
| Dynamic ablation device EE Greenblatt, KI Trovato, TJ Naypauer, S Virmani US Patent App. 13/516,757, 2012 | 57 | 2012 |
| Age-and anatomy-related values of blood-brain barrier permeability measured by perfusion-CT in non-stroke patients JW Dankbaar, J Hom, T Schneider, SC Cheng, BC Lau, I van der Schaaf, ... Journal of neuroradiology 36 (4), 219-227, 2009 | 32 | 2009 |
| Lessons learned from translating AI from development to deployment in healthcare K Widner, S Virmani, J Krause, J Nayar, R Tiwari, ER Pedersen, D Jeji, ... Nature Medicine 29 (6), 1304-1306, 2023 | 31 | 2023 |
| Fully automated segmentation of carotid and vertebral arteries from contrast-enhanced CTA O Cuisenaire, S Virmani, ME Olszewski, R Ardon Medical Imaging 2008: Image Processing 6914, 1210-1217, 2008 | 22 | 2008 |
| Electronic colon-cleansing for CT colonography: diagnostic performance MS Juchems, A Ernst, P Johnson, S Virmani, HJ Brambs, AJ Aschoff Abdominal imaging 34, 359-364, 2009 | 14 | 2009 |
| Accuracy and anatomical coverage of perfusion CT assessment of the blood-brain barrier permeability: one bolus versus two boluses JW Dankbaar, J Hom, T Schneider, SC Cheng, BC Lau, I Van Der Schaaf, ... Cerebrovascular Diseases 26 (6), 600-605, 2008 | 12 | 2008 |
| Towards generalist biomedical ai, 2023 T Tu, S Azizi, D Driess, M Schaekermann, M Amin, PC Chang, A Carroll, ... URL https://arxiv. org/abs/2307.14334, 2023 | 7 | 2023 |
| Dynamic acquisition sampling rate for computed tomography perfusion (CTP) imaging M Vembar, TB Ivanc, S Virmani US Patent 9,955,934, 2018 | 7 | 2018 |
| Fundus imaging apparatus EH Iliffe-Moon, TR Swiss, MR Toh, KD Wood, CT Wing, S Virmani, ... US Patent App. 29/602,209, 2019 | 6 | 2019 |
| Performance of a diabetic retinopathy deep learning model for ultra-widefield imaging T Peto, LP Aiello, SR Sadda, D Lewis, AM Cairns, D Keane, S Virmani, ... Investigative Ophthalmology & Visual Science 63 (7), 587–A0152-587–A0152, 2022 | 4 | 2022 |
| Automatic polyp detection and measurement with computed tomographic colonography: A phantom study S Virmani, AS Lev-Toaff, LM Ciancibello Biomedical Imaging and Intervention Journal 5 (3), 2009 | 3 | 2009 |
| Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study M Schaekermann, T Spitz, M Pyles, H Cole-Lewis, E Wulczyn, SR Pfohl, ... EClinicalMedicine 70, 2024 | 2 | 2024 |
| Single scan multi-procedure imaging S Virmani, TJ Naypauer, DB McKnight US Patent 8,594,406, 2013 | 2 | 2013 |
| Deep learning for predicting the progression of diabetic retinopathy using fundus images A Bora, B Babenko, S Virmani, J Cuadros, S Balasubramanian, ... Investigative Ophthalmology & Visual Science 61 (7), 1639-1639, 2020 | 1 | 2020 |
| The Effect of Iron Deficiency Anemia on Glycated Hemoglobin (HbA1c) in Non Diabetic Adults P Singh, S Singhal, SK Virmani IOSR J Dent Med Sci e-ISSN 16 (2), 26-31, 2017 | 1 | 2017 |
