{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:09:37Z","timestamp":1775066977858,"version":"3.50.1"},"reference-count":80,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>Retinopathy of prematurity (ROP) is a leading cause of blindness in premature infants worldwide, particularly in developing countries. In this research, we propose a Deep Convolutional Neural Network (DCNN) and image processing-based approach for the automatic detection of retinal features, including the optical disc (OD) and retinal blood vessels (BV), as well as disease classification using a rule-based method for ROP patients. Our DCNN model uses YOLO-v5 for OD detection and either Pix2Pix or a U-Net for BV segmentation.<\/jats:p>\n          <jats:p>We trained our DCNN models on publicly available fundus image datasets of size 1,117 and 288 for OD detection and BV segmentation, respectively. We evaluated our approach on a dataset of 439 preterm neonatal retinal images, testing for ROP Zone and 6 BV masks. Our proposed system achieved excellent results, with the OD detection module achieving an overall accuracy of 98.94% (when IoU 0.5) and the BV segmentation module achieving an accuracy of 96.69% and a Dice coefficient between 0.60 and 0.64. Moreover, our system accurately diagnosed ROP in Zone-1 with 88.23% accuracy. Our approach offers a promising solution for accurate ROP screening and diagnosis, particularly in low-resource settings, where it has the potential to improve healthcare outcomes.<\/jats:p>","DOI":"10.1145\/3596223","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T11:47:09Z","timestamp":1684756029000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep Learning-assisted Retinopathy of Prematurity (ROP) Screening"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6840-7913","authenticated-orcid":false,"given":"Vijay","family":"Kumar","sequence":"first","affiliation":[{"name":"Amar Nath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9364-9435","authenticated-orcid":false,"given":"Het","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Delhi, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6970-5509","authenticated-orcid":false,"given":"Kolin","family":"Paul","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Delhi, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8050-5812","authenticated-orcid":false,"given":"Shorya","family":"Azad","sequence":"additional","affiliation":[{"name":"Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, Delhi, India"}]}],"member":"320","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Dirk-Jan Kroon. 2023. 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