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| Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy J Krause, V Gulshan, E Rahimy, P Karth, K Widner, GS Corrado, L Peng, ... Ophthalmology 125 (8), 1264-1272, 2018 | 519 | 2018 |
| Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India V Gulshan, RP Rajan, K Widner, D Wu, P Wubbels, T Rhodes, ... JAMA ophthalmology 137 (9), 987-993, 2019 | 238 | 2019 |
| Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program P Ruamviboonsuk, J Krause, P Chotcomwongse, R Sayres, R Raman, ... NPJ digital medicine 2 (1), 25, 2019 | 223* | 2019 |
| 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 | 126 | 2022 |
| 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 | 36 | 2023 |
| Challenges in evaluating clinical deployments of deep learning assisted diagnostics for diabetic retinopathy screening G Wolff, R Sayres, V Gulshan, K Widner, J Krause, D Jadeja, ... Investigative Ophthalmology & Visual Science 61 (7), 2045-2045, 2020 | 3 | 2020 |
| Real-World Performance of a Deep Learning Diabetic Retinopathy Algorithm P Singh, L Yang, J Nayar, Y Matias, G Corrado, D Wesbter, S Virmani, ... Investigative Ophthalmology & Visual Science 65 (7), 2323-2323, 2024 | | 2024 |
| Validating a Deep Learning based Diabetic Retinopathy Model among Patients with Young-onset diabetes A Tan-Torres, P Pradeep, A Brant, L Yang, P Singh, T Ali, I Traynis, ... Investigative Ophthalmology & Visual Science 65 (7), 5657-5657, 2024 | | 2024 |
Dale R WebsterResearch Director, GoogleVerified email at google.com
