{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T19:11:00Z","timestamp":1793214660021,"version":"4.5.0"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The primary objective of this study was to develop and validate an AI algorithm as a screening tool for the detection of retinopathy of prematurity (ROP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>Images were collected from infants enrolled in the KIDROP tele-ROP screening program.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We developed a deep learning (DL) algorithm with 227,326 wide-field images from multiple camera systems obtained from the KIDROP tele-ROP screening program in India over an 11-year period. 37,477 temporal retina images were utilized with the dataset split into train (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200925,982, 69.33%), validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20094,006, 10.69%), and an independent test set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20097,489, 19.98%). The algorithm consists of a binary classifier that distinguishes between the presence of ROP (Stages 1\u20133) and the absence of ROP. The image labels were retrieved from the daily registers of the tele-ROP program. They consist of per-eye diagnoses provided by trained ROP graders based on all images captured during the screening session. Infants requiring treatment and a proportion of those not requiring urgent referral had an additional confirmatory diagnosis from an ROP specialist.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the 7,489 temporal images analyzed in the test set, 2,249 (30.0%) images showed the presence of ROP. The sensitivity and specificity to detect ROP was 91.46% (95% CI: 90.23%\u201392.59%) and 91.22% (95% CI: 90.42%\u201391.97%), respectively, while the positive predictive value (PPV) was 81.72% (95% CI: 80.37%\u201383.00%), negative predictive value (NPV) was 96.14% (95% CI: 95.60%\u201396.61%) and the AUROC was 0.970.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The novel ROP screening algorithm demonstrated high sensitivity and specificity in detecting the presence of ROP. A prospective clinical validation in a real-world tele-ROP platform is under consideration. It has the potential to lower the number of screening sessions required to be conducted by a specialist for a high-risk preterm infant thus significantly improving workflow efficiency.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2023.1197237","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T23:53:34Z","timestamp":1695340414000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population"],"prefix":"10.3389","volume":"11","author":[{"given":"Divya Parthasarathy","family":"Rao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian M.","family":"Savoy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua Zhi 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