| Pan-cancer image-based detection of clinically actionable genetic alterations JN Kather, LR Heij, HI Grabsch, C Loeffler, A Echle, HS Muti, J Krause, ... Nature cancer 1 (8), 789-799, 2020 | 429 | 2020 |
| Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology JM Dolezal, A Srisuwananukorn, D Karpeyev, S Ramesh, S Kochanny, ... Nature communications 13 (1), 6572, 2022 | 54 | 2022 |
| Clinical, laboratory, and genetic risk factors for thrombosis in sickle cell disease A Srisuwananukorn, R Raslan, X Zhang, BN Shah, J Han, M Gowhari, ... Blood Advances 4 (9), 1978-1986, 2020 | 38 | 2020 |
| Reduced skeletal muscle function is associated with decreased fiber cross-sectional area in the Cy/+ rat model of progressive kidney disease JM Organ, A Srisuwananukorn, P Price, JE Joll, KC Biro, JE Rupert, ... Nephrology Dialysis Transplantation 31 (2), 223-230, 2016 | 31 | 2016 |
| Deep learning detects virus presence in cancer histology JN Kather, J Schulte, HI Grabsch, C Loeffler, H Muti, J Dolezal, ... BioRxiv, 690206, 2019 | 21 | 2019 |
| Deep learning generates synthetic cancer histology for explainability and education JM Dolezal, R Wolk, HM Hieromnimon, FM Howard, A Srisuwananukorn, ... NPJ Precision Oncology 7 (1), 49, 2023 | 13 | 2023 |
| Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence FM Howard, J Dolezal, S Kochanny, G Khramtsova, J Vickery, ... NPJ Breast Cancer 9 (1), 25, 2023 | 10 | 2023 |
| European LeukemiaNet response predicts disease progression but not thrombosis in polycythemia vera D Tremblay, A Srisuwananukorn, L Ronner, N Podoltsev, J Gotlib, ... Hemasphere 6 (6), e721, 2022 | 10 | 2022 |
| In vivo reference point indentation measurement variability in skeletally mature inbred mice A Srisuwananukorn, MR Allen, DM Brown, JM Wallace, JM Organ BoneKEy reports 4, 2015 | 9 | 2015 |
| European LeukemiaNet (ELN) response predicts disease progression but not thrombosis or death in polycythemia vera (PV): an analysis of a multicenter database D Tremblay, A Srisuwananukorn, L Ronner, N Podoltsev, J Gotlib, ... Blood 138, 240, 2021 | 7 | 2021 |
| Slideflow: deep learning for digital histopathology with real-time whole-slide visualization JM Dolezal, S Kochanny, E Dyer, S Ramesh, A Srisuwananukorn, ... BMC bioinformatics 25 (1), 134, 2024 | 5 | 2024 |
| Antimicrobial resistance is a risk factor for mortality in adults with sickle cell disease A Srisuwananukorn, J Han, R Raslan, M Gowhari, F Hussain, F Njoku, ... haematologica 106 (6), 1745, 2021 | 3 | 2021 |
| Accounting for early job turnover in recent pediatric surgery fellowship graduates: An American Pediatric Surgical Association Membership and Credentials Committee study TD Crafts, TM Bell, A Srisuwananukorn, H Applebaum, TA Markel Journal of pediatric surgery 53 (11), 2273-2278, 2018 | 3 | 2018 |
| Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary A Srisuwananukorn, ME Salama, AT Pearson Haematologica 108 (8), 1993, 2023 | 2 | 2023 |
| Multimodal prediction of breast cancer recurrence assays and risk of recurrence FM Howard, J Dolezal, S Kochanny, G Khramtsova, J Vickery, ... bioRxiv, 2022.07. 07.499039, 2022 | 2 | 2022 |
| A novel machine learning-derived dynamic scoring system predicts risk of thrombosis in polycythemia vera (PV) patients G Abu-Zeinah, S Krichevsky, RT Silver, E Taylor III, D Tremblay, ... Blood 138, 3619, 2021 | 2 | 2021 |
| Effects of renin‐angiotensin blockade and APOL1 on kidney function in sickle cell disease J Han, A Srisuwananukorn, BN Shah, RE Molokie, JP Lash, VR Gordeuk, ... EJHaem 2 (3), 483, 2021 | 2 | 2021 |
| Potential contribution of pulmonary thromboembolic disease in pulmonary hypertension in sickle cell disease M Nouraie, X Zhang, A Srisuwananukorn, RF Machado, VR Gordeuk, ... Annals of the American Thoracic Society 17 (7), 899-901, 2020 | 2 | 2020 |
| Type 2 diabetes mellitus in patients with sickle cell disease: a population-based longitudinal analysis of three cohorts J Zhou, J Han, EA Nutescu, W Galanter, SM Walton, VR Gordeuk, ... Blood 132, 4817, 2018 | 2 | 2018 |
| Interpretable artificial intelligence (AI) differentiates prefibrotic primary myelofibrosis (prePMF) from essential thrombocythemia (ET): a multi-center study of a new clinical … A Srisuwananukorn, GG Loscocco, AT Kuykendall, JM Dolezal, R Santi, ... Blood 142, 901, 2023 | 1 | 2023 |
