Editor’s Note: Ophthalmology Times is pleased to introduce a blog series on artificial intelligence (AI), called “Innovations in Ophthalmology.” The first installment in this series provides an overview of AI in medicine. Look for future blogs that will delve into more specific speclatiies within ophthalmology.
Artificial intelligence (AI) has made its way into medicine. Once an AI system gains the ability to recognize patterns or markers of a disease, it can become a tool for automated diagnosis. AI systems are already available or in development for the detection of multiple ophthalmic diseases, including diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma.
The earliest forms of medical AI were simple automated detectors, designed to recognize a defined set of disease features that were programmed into the system. A limitation of these early systems is that they will recognize only patients who express features that are included in the defined program.1
The most advanced iteration of medical AI teaches itself the features of disease by analyzing a representative set of images from people with and without the disease, and possibly across various stages of disease. During the learning phase, the system performs multiple rounds of analysis, assessment, and re-analysis until each image can be faithfully identified. In contrast to simple automated detectors, AI systems that self-teach (called deep learning with convolutional neural networks) are unconstrained in the number of disease features that they may identify.1
How AI benefits patients and physicians
Automated AI systems have multiple benefits that will advance clinical practice. By design, an AI system is a tool for automated diagnosis, which can reduce burden in a care setting with limited physician resources. An AI system can perform its function anywhere, which means that data can be collected remotely and sent to an AI center for analysis. This is basically an AI version of telemedicine, where you can provide a diagnosis and other expert guidance without the need for travel (patient or physician), increasing access to care for patients who live in remote or difficult-to-reach areas, and reduces the burden on patients, care givers and physicians.
AI systems can quickly process a large amount of data, which means that you can analyze a full series of closely spaced scans that cover a wide area of retina from a single patient. This increases the probability of identifying early-stage disease that may only show features in small isolated areas. Improving the ability to detect early-stage disease is significant, because outcomes are often best when treatment is initiated at the beginning of a disease course, before the tissue has been damaged beyond repair.
In addition to advancing clinical practice, deep learning AI systems might contribute to an improved understanding of disease mechanisms. Deep learning AI has the potential to identify previously unknown patterns of disease that would improve our understanding of the pathogenesis of disease, and provide additional markers for diagnosis, staging, and prognosis.1
Rohit Varma, MD, MPH
Dr. Varma is the founding director of the USC Roski Eye Institute and a glaucoma specialist. He also holds the Grace and Emery Beardsley chair in Ophthalmology, and is a professor of preventive medicine at the Keck School of Medicine of University of Southern California, Los Angeles.
1. Roach L. Artificial intelligence. EyeNet Magazine. November 2017;77-83.
2. 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;316:2402-2410.
3. Simonite T. Google’s AI eye doctor gets ready to go to work in India. Wired Magazine. June 8, 2017. www.wired.com/2017/06/googles-ai-eye-doctor-gets-ready-go-work-india/. Accessed April 19, 2018.
4. van der Heijden AA, Abramoff MD, Verbraak F, et al. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmol. 2018;96:63-68.
5. FDA permits marketing of IDx-DR for automated detection of diabetic retinopathy in primary care [press release]. IDx. April 12, 2018. www.eyediagnosis.net/single-post/2018/04/12/FDA-permits-marketing-of-IDx.... Accessed April 19, 2018.
6. Bogunović H, Montuoro A, Baratsits M, et al. Machine learning of the progression of intermediate age-related macular degeneration based on OCT imaging. Invest Ophthalmol Vis Sci. 2017;58:BIO141-BIO150.
7. Bogunović H, Waldstein SM, Schlelgl T, et al. Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach. Invest Ophthalmol Vis Sci. 2017;58:3240-3248.
8. Schlegl T, Waldstein SM, Bogunovic H, et al. Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology. 2018;125:549-558.
9. Jiu X, Jiang J, Zhang K, et al. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PLoS One. 2017;12:e0168606.
10. Kim SJ, Cho KJ, Oh S. Development of machine learning models for diagnosis of glaucoma. PLoS One. 2017;12:e0177726.
11. Ruiz Hidalgo I, Rozema JJ, Saad A, et al. Validation of an objective keratoconus detection system implemented in a Scheimpflug tomographer and comparison with other methods. Cornea. 2017;36:689-695.
12. Ross C. Opening the ‘black box,’ Google DeepMind AI system diagnoses eye diseases and shows its work. STAT Magazine. August 13, 2017. https://www.statnews.com/2018/08/13/google-deepmind-ai-diagnoses-eye-dis.... Accessed August 14, 2018.
13. De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. Epub ahead of print Aug 13 2018. doi: 10.1038/s41591-018-0107-6.