دورية أكاديمية
أعرض في EDS

Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia.

التفاصيل البيبلوغرافية
العنوان: Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia.
المؤلفون: Cole E; Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois., Valikodath NG; Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois., Al-Khaled T; Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois., Bajimaya S; Tilganga Institute of Ophthalmology, Kathmandu, Nepal., Kc S; Helen Keller International, Kathmandu, Nepal., Chuluunbat T; National Center for Maternal and Child Health, Ulaanbaatar, Mongolia., Munkhuu B; National Center for Maternal and Child Health, Ulaanbaatar, Mongolia., Jonas KE; Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois., Chuluunkhuu C; Orbis International, New York, New York., MacKeen LD; The Hospital for Sick Children, Toronto, Canada.; Phoenix Technology Group, Pleasanton, California., Yap V; Department of Pediatrics, Weill Cornell Medical College, New York, New York., Hallak J; Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois., Ostmo S; Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon., Wu WC; Chang Gung Memorial Hospital, Taoyuan, Taiwan, and Chang Gung University, College of Medicine, Taoyuan, Taiwan., Coyner AS; Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon., Singh P; Harvard Medical School, Boston, Massachusetts., Kalpathy-Cramer J; Harvard Medical School, Boston, Massachusetts., Chiang MF; National Eye Institute, National Institutes of Health, Bethesda, Maryland., Campbell JP; Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon., Chan RVP; Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois.
المصدر: Ophthalmology science [Ophthalmol Sci] 2022 Apr 25; Vol. 2 (4), pp. 100165. Date of Electronic Publication: 2022 Apr 25 (Print Publication: 2022).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier, B.V Country of Publication: Netherlands NLM ID: 9918230896206676 Publication Model: eCollection Cited Medium: Internet ISSN: 2666-9145 (Electronic) Linking ISSN: 26669145 NLM ISO Abbreviation: Ophthalmol Sci Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [Amsterdam] : Elsevier, B.V., [2021]-
مستخلص: Purpose: To evaluate the performance of a deep learning (DL) algorithm for retinopathy of prematurity (ROP) screening in Nepal and Mongolia.
Design: Retrospective analysis of prospectively collected clinical data.
Participants: Clinical information and fundus images were obtained from infants in 2 ROP screening programs in Nepal and Mongolia.
Methods: Fundus images were obtained using the Forus 3nethra neo (Forus Health) in Nepal and the RetCam Portable (Natus Medical, Inc.) in Mongolia. The overall severity of ROP was determined from the medical record using the International Classification of ROP (ICROP). The presence of plus disease was determined independently in each image using a reference standard diagnosis. The Imaging and Informatics for ROP (i-ROP) DL algorithm was trained on images from the RetCam to classify plus disease and to assign a vascular severity score (VSS) from 1 through 9.
Main Outcome Measures: Area under the receiver operating characteristic curve and area under the precision-recall curve for the presence of plus disease or type 1 ROP and association between VSS and ICROP disease category.
Results: The prevalence of type 1 ROP was found to be higher in Mongolia (14.0%) than in Nepal (2.2%; P  < 0.001) in these data sets. In Mongolia (RetCam images), the area under the receiver operating characteristic curve for examination-level plus disease detection was 0.968, and the area under the precision-recall curve was 0.823. In Nepal (Forus images), these values were 0.999 and 0.993, respectively. The ROP VSS was associated with ICROP classification in both datasets ( P < 0.001). At the population level, the median VSS was found to be higher in Mongolia (2.7; interquartile range [IQR], 1.3-5.4]) as compared with Nepal (1.9; IQR, 1.2-3.4; P < 0.001).
Conclusions: These data provide preliminary evidence of the effectiveness of the i-ROP DL algorithm for ROP screening in neonatal populations in Nepal and Mongolia using multiple camera systems and are useful for consideration in future clinical implementation of artificial intelligence-based ROP screening in low- and middle-income countries.
(© 2022 by the American Academy of Ophthalmology.)
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فهرسة مساهمة: Keywords: Artificial intelligence; BW, birth weight; DL, deep learning; Deep learning; GA, gestational age; ICROP, International Classification of Retinopathy of Prematurity; IQR, interquartile range; LMIC, low- and middle-income country; Mongolia; Nepal; ROP, retinopathy of prematurity; RSD, reference standard diagnosis; Retinopathy of prematurity; TR, treatment-requiring; VSS, vascular severity score; i-ROP, Imaging and Informatics for Retinopathy of Prematurity
تواريخ الأحداث: Date Created: 20221219 Latest Revision: 20221221
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC9754980
DOI: 10.1016/j.xops.2022.100165
PMID: 36531583
قاعدة البيانات: MEDLINE
الوصف
تدمد:2666-9145
DOI:10.1016/j.xops.2022.100165