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Multinational External Validation of Autonomous Retinopathy of Prematurity Screening.
Coyner, Aaron S; Murickan, Tom; Oh, Minn A; Young, Benjamin K; Ostmo, Susan R; Singh, Praveer; Chan, R V Paul; Moshfeghi, Darius M; Shah, Parag K; Venkatapathy, Narendran; Chiang, Michael F; Kalpathy-Cramer, Jayashree; Campbell, J Peter.
Affiliation
  • Coyner AS; Casey Eye Institute, Oregon Health & Science University, Portland.
  • Murickan T; Casey Eye Institute, Oregon Health & Science University, Portland.
  • Oh MA; Casey Eye Institute, Oregon Health & Science University, Portland.
  • Young BK; Casey Eye Institute, Oregon Health & Science University, Portland.
  • Ostmo SR; Casey Eye Institute, Oregon Health & Science University, Portland.
  • Singh P; Ophthalmology, University of Colorado School of Medicine, Aurora.
  • Chan RVP; Illinois Eye and Ear Infirmary, University of Illinois at Chicago.
  • Moshfeghi DM; Byers Eye Institute, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, California.
  • Shah PK; Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India.
  • Venkatapathy N; Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India.
  • Chiang MF; National Eye Institute, National Institutes of Health, Bethesda, Maryland.
  • Kalpathy-Cramer J; National Library of Medicine, National Institutes of Health, Bethesda, Maryland.
  • Campbell JP; Ophthalmology, University of Colorado School of Medicine, Aurora.
JAMA Ophthalmol ; 142(4): 327-335, 2024 Apr 01.
Article in En | MEDLINE | ID: mdl-38451496
ABSTRACT
Importance Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening.

Objective:

To evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP. Design, Setting, and

Participants:

This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023. Exposures An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine. Main Outcomes and

Measures:

The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels.

Results:

The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP 100%; 95% CI, 81.4-100; AECS 100%; 95% CI, 94.7-100) prior to diagnosis. Conclusions and Relevance Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: Retinopathy of Prematurity Limits: Child / Humans / Infant / Newborn Language: En Year: 2024 Type: Article

Full text: 1 Database: MEDLINE Main subject: Retinopathy of Prematurity Limits: Child / Humans / Infant / Newborn Language: En Year: 2024 Type: Article