Deep learning could have an application in the diagnosis and management of retinal diseases. Here’s what clinicians need to know.
Deep learning, a type of artificial intelligence, uses algorithms to recognize patterns. Deep learning holds considerable promise in medicine and may assist physicians in evaluating medical imaging for faster, more accurate diagnoses. Although the hope is that deep learning can transform and disrupt healthcare, how exactly to harness the technology is still being studied.
It does, however, appear that deep learning could have an application in the diagnosis and management of retinal diseases. A number of studies have demonstrated the accuracy of deep-learning algorithms in diagnosing diabetic retinopathy and diabetic macular edema from fundus photographs.1, 2
Most recently, a study from Gulshan et al published in JAMA Ophthalmology assessed the performance of a deep-learning algorithm versus manual grading for diabetic retinopathy in India.3
The prospective, observational study included a little more than 3,000 patients age 40 or older with diabetes from two tertiary eye care centers. Patients were excluded if they had a history of any intraocular surgery other than cataract surgery, ocular laser treatments for retinal disease, ocular injections for diabetic macular edema, or a history of other retinal vascular diseases or glaucoma.
The automated, deep-learning grading system was compared against manual grading by a trained grader and a retinal specialist from each center. A panel of three retinal specialists adjudicated any disagreements between the retinal specialists and trained grader, when needed.
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.