Deep learning could have an application in the diagnosis and management of retinal diseases. Here’s what clinicians need to know.
The automated deep-learning system was able to correctly identify referable diabetic retinopathy with equal or better accuracy than the retina specialists and trained graders. Importantly, the results were consistent across both eye care centers.
On the combined dataset, the sensitivity and specificity of the manual graders ranged from 73.4% to 89.8% and 83.5% and 98.7%, respectively. Similarly, the deep-learning system ranged from 88.9% to 92.1% and 92.2% to 95.2% for sensitivity and specificity, respectively.
The researchers argue that their work demonstrates the potential of using an automated grading system for the diagnosis of diabetic retinopathy and diabetic macular edema.
If proven successful, deep-learning technology could make screening for retinal diseases more cost-effective and efficient.
A commentary from Ting DSW et al. in the same issue of JAMA Ophthalmology noted the value of testing deep-learning algorithms in the real-world setting, especially in lower-resource countries such as India. India has one of the largest burdens of diabetes in the world. More than 60 million people are at risk for developing diabetic retinopathy, yet the country lacks a unified strategy for screening and has a shortage of trained eye care specialists.3 An automated system could help scale screenings and bridge the workforce gap.
1. Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402-2410.
2. Ting DSW, Cheung CY, Lim G, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017;318(22):2211-2223.
3. Gulshan V, Rajan RP, Widner K, et al. Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India. JAMA Ophthalmol. 2019.
4. TY W. Artificial Intelligence for Diabetic Retinopathy Screening American Academy of Ophthalmology Retina Subspecialty Day. San Francisco, California 2019.