Kasumi Widner

TL;DR: An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.

TL;DR: Adjudication reduces the errors in DR grading by using a small number of adjudicated consensus grades as a tuning dataset and higher-resolution images as input, and to train an improved automated algorithm for DR grading.

TL;DR: This study shows that the automated DR system generalizes to this population of Indian patients in a prospective setting and demonstrates the feasibility of using an automated DR grading system to expand screening programs.

TL;DR: Across different severity levels of DR for determining referable disease, deep learning significantly reduced the false negative rate at the cost of slightly higher false positive rates (2%).

TL;DR: In this paper , a prospective interventional cohort study to evaluate the real-world performance and feasibility of deploying a deep-learning system into the health-care system of Thailand was conducted, where eligible patients were screened with the deep learning system at nine primary care sites under Thailand's national diabetic retinopathy screening programme.