Deep learning could have an application in the diagnosis and management of retinal diseases. Here’s what clinicians need to know.
Although using deep learning in the retinal space seems possible, and perhaps practical in some settings, Ting et al stress the importance of evaluating automation in a clinical research setting and in the United States before deploying it in the clinic. There’s also physician intuition and experience at play that cannot be replaced by automation.
For example, in a commentary for the American Academy of Ophthalmology 2019 Retina Subspecialty Day, Tien Yin Wong, MBBS, noted that the diagnosis of diabetic macular edema requires accurate interpretation of OCT screenings in addition to retinal photographs.4
This requires specialized knowledge and expertise, and it’s unclear if a machine is capable of replicating the entire process.
Finally, healthcare professionals must develop a strategy to manage false negatives before the technology is ready for prime time.
Deep learning may have several challenges to overcome before it becomes a mainstay in the clinic, but one thing is for certain: artificial intelligence in medicine is here to stay.
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.