Diabetic retinopathy classification for supervised machine learning algorithms

Abstract

Background Artificial intelligence and automated technology were first reported more than 70 years ago and nowadays provide unprecedented diagnostic accuracy, screening capacity, risk stratification, and workflow optimization. Diabetic retinopathy is an important cause of preventable blindness worldwide, and artificial intelligence technology provides precocious diagnosis, monitoring, and guide treatment. High-quality exams are fundamental in supervised artificial intelligence algorithms, but the lack of ground truth standards in retinal exams datasets is a problem. Main body In this article, ETDRS, NHS, ICDR, SDGS diabetic retinopathy grading, and manual annotation are described and compared in publicly available datasets. The various DR labeling systems generate a fundamental problem for AI datasets. Possible solutions are standardization of DR classification and direct retinal-finding identifications. Conclusion Reliable labeling methods also need to be considered in datasets with more trustworthy labeling.

An overview of artificial intelligence use in diabetic retinopathy treatment: a narrative review

“…This approach establishes an accurate mapping from the image to the region by using numerous images of the macular and optic discs that have already been tagged. Using a kernel least-squares classifier to determine the optic disc area, the researchers developed a method to identify the optic disc region and pinpoint its location for retinal color pictures [42].…”

Is it time to consider teleophthalmology as a game-changer in the management of diabetic retinopathy?

“…likely ready for clinical applications". However, it is worth noting that recently Nakayama et al (61) discussed the utilization of supervised ML algorithms for DR classification and have emphasized a need for the implementation of versatile comprehensive classification with equal referral criteria.…”

Impact of sustained hypertension on new cardiovascular events in patients with type 2 diabetes: KAMOGAWA‐HBP study

“…Neuropathy was diagnosed on the basis of the diagnostic criteria for diabetic neuropathy proposed by the Diagnostic Neuropathy Study Group. 11 Retinopathy was classified into three levels: “no diabetic retinopathy,” “simple diabetic retinopathy,” and “proliferative diabetic retinopathy.” 12 Macrovascular complications were defined as the presence of a history of cerebrovascular disease, cardiovascular disease, or atherosclerosis obliterans, based on the patient's medical history.…”

“…and "proliferative diabetic retinopathy." 12 Macrovascular complications were defined as the presence of a history of cerebrovascular disease, cardiovascular disease, or atherosclerosis obliterans, based on the patient's medical history.…”