Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma

Simple item page
dc.contributor.authorSurya, Jananien_US
dc.contributor.authorGarima,en_US
dc.contributor.authorPandy, Nehaen_US
dc.contributor.authorHyungtaek Rim, Tyleren_US
dc.contributor.authorLee, Geunyoungen_US
dc.contributor.authorPriya, MN Swathien_US
dc.contributor.authorSubramanian, Brughanyaen_US
dc.contributor.authorRaman, Rajiven_US
dc.date.accessioned2023-08-25T06:38:01Z
dc.date.available2023-08-25T06:38:01Z
dc.date.issued2023-08
dc.description.abstractPurpose: To analyze the efficacy of a deep learning (DL)?based artificial intelligence (AI)?based algorithm in detecting the presence of diabetic retinopathy (DR) and glaucoma suspect as compared to the diagnosis by specialists secondarily to explore whether the use of this algorithm can reduce the cross?referral in three clinical settings: a diabetologist clinic, retina clinic, and glaucoma clinic. Methods: This is a prospective observational study. Patients between 35 and 65 years of age were recruited from glaucoma and retina clinics at a tertiary eye care hospital and a physician’s clinic. Non?mydriatic fundus photography was performed according to the disease?specific protocols. These images were graded by the AI system and specialist graders and comparatively analyzed. Results: Out of 1085 patients, 362 were seen at glaucoma clinics, 341 were seen at retina clinics, and 382 were seen at physician clinics. The kappa agreement between AI and the glaucoma grader was 85% [95% confidence interval (CI): 77.55–92.45%], and retina grading had 91.90% (95% CI: 87.78–96.02%). The retina grader from the glaucoma clinic had 85% agreement, and the glaucoma grader from the retina clinic had 73% agreement. The sensitivity and specificity of AI glaucoma grading were 79.37% (95% CI: 67.30–88.53%) and 99.45 (95% CI: 98.03–99.93), respectively; DR grading had 83.33% (95 CI: 51.59–97.91) and 98.86 (95% CI: 97.35–99.63). The cross?referral accuracy of DR and glaucoma was 89.57% and 95.43%, respectively. Conclusion: DL?based AI systems showed high sensitivity and specificity in both patients with DR and glaucoma; also, there was a good agreement between the specialist graders and the AI systemen_US
dc.identifier.affiliationsDepartment of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya,Chennai, Tamil Nadu, Indiaen_US
dc.identifier.affiliationsDepartment of Ophthalmology, Singapore Eye Research Institute, Singapore National Eye Centre, Singaporeen_US
dc.identifier.affiliationsDepartment of Science and Technology, Medi Whale, Seoul, South Koreaen_US
dc.identifier.citationSurya Janani, Garima , Pandy Neha, Hyungtaek Rim Tyler, Lee Geunyoung, Priya MN Swathi, Subramanian Brughanya, Raman Rajiv. Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma. Indian Journal of Ophthalmology. 2023 Aug; 71(8): 3039-3045en_US
dc.identifier.issn1998-3689
dc.identifier.issn0301-4738
dc.identifier.placeIndiaen_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/225176
dc.languageenen_US
dc.publisherAll India Ophthalmological Societyen_US
dc.relation.issuenumber8en_US
dc.relation.volume71en_US
dc.source.urihttps://doi.org/10.4103/IJO.IJO_11_23en_US
dc.subjectArtificial intelligenceen_US
dc.subjectdeep learningen_US
dc.subjectdiabetic retinopathyen_US
dc.subjectand glaucomaen_US
dc.titleEfficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucomaen_US
dc.typeJournal Articleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
IJO2023v71n8p3039.pdf
Size:
1.93 MB
Format:
Adobe Portable Document Format
Download
Collections
Indian Journal of Ophthalmology