In The News

Tech that detects cause of preemie blindness gets federal nod

By Franny White January 30, 2020

An artificial intelligence algorithm that can detect a potentially devastating cause of childhood blindness better than most human experts has been granted breakthrough status by the FDA.

The FDA Breakthrough Device Program aims to accelerate development - and potentially approval - of medical devices for “more effective treatment or diagnosis of life-threatening or irreversibly debilitating diseases.”

The algorithm, called the i-ROP DL system, diagnoses retinopathy of prematurity, or ROP. Every year up to 16,000 prematurely born U.S. infants are affected by the disorder, which causes abnormal blood vessel growth near the retina, the light-sensitive portion in the back of an eye. About 600 U.S. babies go blind from ROP annually, making it a leading cause of childhood blindness in the U.S. and worldwide. Musician Stevie Wonder is blind as a result of ROP.

Novel AI Detects Retinopathy of Prematurity with 100% Accuracy

Portland, Oregon March 8, 2024

In a study published in JAMA Ophthalmology, researchers from Oregon Health & Science University (OHSU) alongside international collaborators have unveiled an artificial intelligence (AI) technology capable of independently detecting all severe cases of a condition leading to blindness in prematurely born infants.

If regulatory approvals are secured, ROP will become the second eye disease autonomously detectable by AI, following diabetic retinopathy. This marks a significant leap forward in using AI to enhance healthcare accessibility and effectiveness, particularly in underserved regions, and opens new avenues for preventing blindness in vulnerable populations worldwide.

Press release: Eye care organizations unite to save sight of premature infants using artificial intelligence

New York September 21, 2023

Eye care nonprofit Orbis International is excited to announce a new strategic partnership with Siloam Vision to use the company's cloud-based telemedicine platform to expand access to eye care and prevent blindness in premature infants living in hard-to-reach communities. Siloam Vision's artificial intelligence (AI) platform helps diagnose retinopathy of prematurity (ROP) – the leading cause of childhood blindness globally.

Artificial Intelligence Getting Smarter! Innovations from the Vision Field

By Michael F. Chiang, M.D., National Eye Institute February 8, 2022

One of many health risks premature infants face is retinopathy of prematurity (ROP), a leading cause of childhood blindness worldwide. ROP causes abnormal blood vessel growth in the light-sensing eye tissue called the retina. Left untreated, ROP can lead to lead to scarring, retinal detachment, and blindness. It’s the disease that caused singer and songwriter Stevie Wonder to lose his vision.

Now, effective treatments are available—if the disease is diagnosed early and accurately. Advancements in neonatal care have led to the survival of extremely premature infants, who are at highest risk for severe ROP. Despite major advancements in diagnosis and treatment, tragically, about 600 infants in the U.S. still go blind each year from ROP. This disease is difficult to diagnose and manage, even for the most experienced ophthalmologists. And the challenges are much worse in remote corners of the world that have limited access to ophthalmic and neonatal care.

Artificial intelligence (AI) is helping bridge these gaps. Prior to my tenure as National Eye Institute (NEI) director, I helped develop a system called i-ROP Deep Learning (i-ROP DL), which automates the identification of ROP. In essence, we trained a computer to identify subtle abnormalities in retinal blood vessels from thousands of images of premature infant retinas. Strikingly, the i-ROP DL artificial intelligence system outperformed even international ROP experts [1]. This has enormous potential to improve the quality and delivery of eye care to premature infants worldwide.

Publications

Brown, James M; Campbell, J Peter; Beers, Andrew; Chang, Ken; Ostmo, Susan; Chan, RV Paul; Dy, Jennifer; Erdogmus, Deniz; Ioannidis, Stratis; Kalpathy-Cramer, Jayashree. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmology. Volume 136-7, Page 803-810, 2018 American Medical Association.

Gupta, Kishan; Campbell, J Peter; Taylor, Stanford; Brown, James M; Ostmo, Susan; Chan, RV Paul; Dy, Jennifer; Erdogmus, Deniz; Ioannidis, Stratis; Kalpathy-Cramer, Jayashree. A quantitative severity scale for retinopathy of prematurity using deep learning to monitor disease regression after treatment. JAMA Ophthalmology. Volume 137-9, Page 1029-1036. 2019 American Medical Association.

Taylor, Stanford; Brown, James M; Gupta, Kishan; Campbell, J Peter; Ostmo, Susan; Chan, RV Paul; Dy, Jennifer; Erdogmus, Deniz; Ioannidis, Stratis; Kim, Sang J. Monitoring disease progression with a quantitative severity scale for retinopathy of prematurity using deep learning. JAMA Ophthalmology. Volume 137-9, Page 1022-1028. 2019 American Medical Association.

Bellsmith, Kellyn N; Brown, James; Kim, Sang Jin; Goldstein, Isaac H; Coyner, Aaron; Ostmo, Susan; Gupta, Kishan; Chan, RV Paul; Kalpathy-Cramer, Jayashree; Chiang, Michael F. Aggressive posterior retinopathy of prematurity: clinical and quantitative imaging features in a large North American cohort. Ophthalmology. Volume 127-8, Page 1105-1112. 2020 Elsevier.

Greenwald, Miles F; Danford, Ian D; Shahrawat, Malika; Ostmo, Susan; Brown, James; Kalpathy-Cramer, Jayashree; Bradshaw, Kacy; Schelonka, Robert; Cohen, Howard S; Chan, RV Paul. Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity. Journal of American Association for Pediatric Ophthalmology and Strabismus. Volume 24-3, Page 160-162. 2020 Mosby.

Choi, Rene Y; Brown, James M; Kalpathy-Cramer, Jayashree; Chan, RV Paul; Ostmo, Susan; Chiang, Michael F; Campbell, J Peter; Kim, Sang Jin; Sonmez, Kemal; Jonas, Karyn. Variability in plus disease identified using a deep learning-based retinopathy of prematurity severity scale. Ophthalmology Retina. Volume 4-10, Page 1016-1021. 2020 Elsevier.

Campbell, J Peter; Kim, Sang Jin; Brown, James M; Ostmo, Susan; Chan, RV Paul; Kalpathy-Cramer, Jayashree; Chiang, Michael F; Sonmez, Kemal; Schelonka, Robert; Jonas, Karyn. Evaluation of a deep learning–derived quantitative retinopathy of prematurity severity scale. Ophthalmology. Volume 128-7, Page 1070-1076. 2021 Elsevier.

Campbell, J Peter; Singh, Praveer; Redd, Travis K; Brown, James M; Shah, Parag K; Subramanian, Prema; Rajan, Renu; Valikodath, Nita; Cole, Emily; Ostmo, Susan. Applications of artificial intelligence for retinopathy of prematurity screening. Pediatrics. Volume 147-3. 2021 American Academy of Pediatrics.

Morrison, Steven; Chan, RV Paul; Chiang, Michael F; Campbell, J Peter. Cost-Effectiveness of Artificial Intelligence-Based Retinopathy of Prematurity Screening. Investigative Ophthalmology & Visual Science. Volume 62-8, Page 3258-3258. 2021 The Association for Research in Vision and Ophthalmology.

Coyner, Aaron S; Chen, Jimmy S; Singh, Praveer; Schelonka, Robert L; Jordan, Brian K; McEvoy, Cindy T; Anderson, Jamie E; Chan, RV; Sonmez, Kemal; Erdogmus, Deniz. Single-examination risk prediction of severe retinopathy of prematurity. Pediatrics. Volume 148-6. 2021 American Academy of Pediatrics.

Campbell, J Peter; Chiang, Michael F; Chen, Jimmy S; Moshfeghi, Darius M; Nudleman, Eric; Ruambivoonsuk, Paisan; Cherwek, Hunter; Cheung, Carol Y; Singh, Praveer; Kalpathy-Cramer, Jayashree. Artificial intelligence for retinopathy of prematurity: validation of a vascular severity scale against international expert diagnosis. Ophthalmology. Volume 129-7, Page e69-e76. 2022 Elsevier.

Lemay, Andreanne; Hoebel, Katharina; Bridge, Christopher P; Befano, Brian; De Sanjosé, Silvia; Egemen, Didem; Rodriguez, Ana Cecilia; Schiffman, Mark; Campbell, John Peter; Kalpathy-Cramer, Jayashree. Improving the repeatability of deep learning models with Monte Carlo dropout. Npj Digital Medicine. Volume 5-1, Page 174. 2022 Nature Publishing Group UK London.

Cole, Emily; Valikodath, Nita G; Al-Khaled, Tala; Bajimaya, Sanyam; Sagun, KC; Chuluunbat, Tsengelmaa; Munkhuu, Bayalag; Jonas, Karyn E; Chuluunkhuu, Chimgee; MacKeen, Leslie D. Evaluation of an artificial intelligence system for retinopathy of prematurity screening in Nepal and Mongolia. Ophthalmology Science. Volume 2-4, Page 100-165. 2022 Elsevier.

Cole, Emily D; Park, Shin Hae; Kim, Sang Jin; Kang, Kai B; Valikodath, Nita G; Al-Khaled, Tala; Patel, Samir N; Jonas, Karyn E; Ostmo, Susan; Coyner, Aaron. Variability in plus disease diagnosis using single and serial images. Ophthalmology retina. Volume 6-12, Page 1122-1129. 2022 Elsevier.

Coyner, Aaron S; Oh, Minn A; Shah, Parag K; Singh, Praveer; Ostmo, Susan; Valikodath, Nita G; Cole, Emily; Al-Khaled, Tala; Bajimaya, Sanyam; Sagun, KC. External validation of a retinopathy of prematurity screening model using artificial intelligence in 3 low-and middle-income populations. JAMA ophthalmology. Volume 140-8, Page 791-798. 2022 American Medical Association.

Eilts, Sonja K; Pfeil, Johanna M; Poschkamp, Broder; Krohne, Tim U; Eter, Nicole; Barth, Teresa; Guthoff, Rainer; Lagrèze, Wolf; Grundel, Milena; Bründer, Marie-Christine. Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence–Based Vascular Severity Score. JAMA Network Open. Volume 6-1, Page e2251512-e2251512. 2023 American Medical Association.

DeCampos-Stairiker, Mallory A; Coyner, Aaron S; Gupta, Aditi; Oh, Minn; Shah, Parag K; Subramanian, Prema; Venkatapathy, Narendran; Singh, Praveer; Kalpathy-Cramer, Jayashree; Chiang, Michael F. Epidemiologic evaluation of retinopathy of prematurity severity in a large telemedicine program in India using artificial intelligence. Ophthalmology. 2023 Elsevier.