Artificial intelligence–based stratification of demographic, ocular surface high-risk factors in progression of keratoconus

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dc.contributor.authorKundu, Gairiken_US
dc.contributor.authorShetty, Narenen_US
dc.contributor.authorShetty, Rohiten_US
dc.contributor.authorKhamar, Poojaen_US
dc.contributor.authorD’Souza, Sharonen_US
dc.contributor.authorMeda, Tulasi Ren_US
dc.contributor.authorNuijts, Rudy M M Aen_US
dc.contributor.authorNarasimhan, Raghaven_US
dc.contributor.authorRoy, Abhijit Sinhaen_US
dc.date.accessioned2023-08-25T06:37:01Z
dc.date.available2023-08-25T06:37:01Z
dc.date.issued2023-05
dc.description.abstractPurpose: The purpose of this study was to identify and analyze the clinical and ocular surface risk factors influencing the progression of keratoconus (KC) using an artificial intelligence (AI) model. Methods: This was a prospective analysis in which 450 KC patients were included. We used the random forest (RF) classifier model from our previous study (which evaluated longitudinal changes in tomographic parameters to predict “progression” and “no progression”) to classify these patients. Clinical and ocular surface risk factors were determined through a questionnaire, which included presence of eye rubbing, duration of indoor activity, usage of lubricants and immunomodulator topical medications, duration of computer use, hormonal disturbances, use of hand sanitizers, immunoglobulin E (IgE), and vitamins D and B12 from blood investigations. An AI model was then built to assess whether these risk factors were linked to the future progression versus no progression of KC. The area under the curve (AUC) and other metrics were evaluated. Results: The tomographic AI model classified 322 eyes as progression and 128 eyes as no progression. Also, 76% of the cases that were classified as progression (from tomographic changes) were correctly predicted as progression and 67% of cases that were classified as no progression were predicted as no progression based on clinical risk factors at the first visit. IgE had the highest information gain, followed by presence of systemic allergies, vitamin D, and eye rubbing. The clinical risk factors AI model achieved an AUC of 0.812. Conclusion: This study demonstrated the importance of using AI for risk stratification and profiling of patients based on clinical risk factors, which could impact the progression in KC eyes and help manage them betteren_US
dc.identifier.affiliationsDepartments of Cornea and Refractive Surgery, Narayana Nethralaya, Bengaluru, Karnataka, Indiaen_US
dc.identifier.affiliationsCataract and Refractive Surgery, Narayana Nethralaya, Bengaluru, Karnataka, Indiaen_US
dc.identifier.affiliationsDepartment of Ophthalmology, Maastricht University Medical Center, Maastricht, The Netherlandsen_US
dc.identifier.affiliationsImaging, Biomechanics and Mathematical Modeling Solutions, Narayana Nethralaya Foundation, Bengaluru, Karnataka, Indiaen_US
dc.identifier.citationKundu Gairik, Shetty Naren, Shetty Rohit, Khamar Pooja, D’Souza Sharon, Meda Tulasi R, Nuijts Rudy M M A, Narasimhan Raghav, Roy Abhijit Sinha. Artificial intelligence–based stratification of demographic, ocular surface high-risk factors in progression of keratoconus. Indian Journal of Ophthalmology. 2023 May; 71(5): 1882-1888en_US
dc.identifier.issn1998-3689
dc.identifier.issn0301-4738
dc.identifier.placeIndiaen_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/224995
dc.languageenen_US
dc.publisherAll India Ophthalmological Societyen_US
dc.relation.issuenumber5en_US
dc.relation.volume71en_US
dc.source.urihttps://doi.org/10.4103/IJO.IJO_2651_22en_US
dc.subjectArtificial intelligenceen_US
dc.subjectdemographicsen_US
dc.subjectkeratoconusen_US
dc.subjectprogressionen_US
dc.subjecttomographyen_US
dc.titleArtificial intelligence–based stratification of demographic, ocular surface high-risk factors in progression of keratoconusen_US
dc.typeJournal Articleen_US
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