Evidence Based Prediction and Progression Monitoring on Retinal Images from Three Nations.

Purpose: The aim of this work is to demonstrate how a retinal image analysis system, DAPHNE, supports the optimization of diabetic retinopathy (DR) screening programs for grading color fundus photography. Method: Retinal image sets, graded by trained and certified human graders, were acquired from Saudi Arabia, China, and Kenya. Each image was subsequently analyzed by the DAPHNE automated software. The sensitivity, specificity, and positive and negative predictive values for the detection of referable DR or diabetic macular edema were evaluated, taking human grading or clinical assessment outcomes to be the gold standard. The automated software's ability to identify co-pathology and to correctly label DR lesions was also assessed. Results: In all three datasets the agreement between the automated software and human grading was between 0.84 to 0.88. Sensitivity did not vary significantly between populations (94.28%-97.1%) with specificity ranging between 90.33% to 92.12%. There were excellent negative predictive values above 93% in all image sets. The software was able to monitor DR progression between baseline and follow-up images with the changes visualized. No cases of proliferative DR or DME were missed in the referable recommendations. Conclusions: The DAPHNE automated software demonstrated its ability not only to grade images but also to reliably monitor and visualize progression. Therefore it has the potential to assist timely image analysis in patients with diabetes in varied populations and also help to discover subtle signs of sight-threatening disease onset. Translational Relevance: This article takes research on machine vision and evaluates its readiness for clinical use

Automated feature-based grading and progression analysis of diabetic retinopathy

BACKGROUND: In diabetic retinopathy (DR) screening programmes feature-based grading guidelines are used by human graders. However, recent deep learning approaches have focused on end to end learning, based on labelled data at the whole image level. Most predictions from such software offer a direct grading output without information about the retinal features responsible for the grade. In this work, we demonstrate a feature based retinal image analysis system, which aims to support flexible grading and monitor progression. METHODS: The system was evaluated against images that had been graded according to two different grading systems; The International Clinical Diabetic Retinopathy and Diabetic Macular Oedema Severity Scale and the UK's National Screening Committee guidelines. RESULTS: External evaluation on large datasets collected from three nations (Kenya, Saudi Arabia and China) was carried out. On a DR referable level, sensitivity did not vary significantly between different DR grading schemes (91.2-94.2.0%) and there were excellent specificity values above 93% in all image sets. More importantly, no cases of severe non-proliferative DR, proliferative DR or DMO were missed. CONCLUSIONS: We demonstrate the potential of an AI feature-based DR grading system that is not constrained to any specific grading scheme

Teleophthalmology: an essential tool in the era of the novel coronavirus 2019

The aim of this article is to assess the current state of teleophthalmology given the sudden surge in telemedicine demand in response to the novel coronavirus 2019 (COVID-19). Recommendations and policies from government and national health organizations, combined with social distancing, have led to exponential increases in telemedicine use. Teleophthalmology can be integrated into ophthalmic care delivery. In the emergency room, teleophthalmology can be utilized to triage patients and diagnose common ophthalmic eye diseases. Ophthalmology practices can utilize real-time medicine to conduct many parts of an in-person exam. In cases where more complex diagnostic tools are warranted, a model incorporating telemedicine and focused in-person visits may still be beneficial. Innovative technologies emerging in the market allow for increased remote monitoring, screening, and management of adult and pediatric patients for common eye diseases. COVID-19 created a demand for healthcare delivery that limits in-person examination and potential viral exposure. Teleophthalmology allows ophthalmologists to continue caring for patients while keeping physicians and patients safe. Although challenges still exist, the pandemic has accelerated the adoption of teleophthalmology. As a result, teleophthalmology will play an integral role in providing high-quality efficient care in the near future