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Patient selection for corneal topographic evaluation of keratoconus: A screening approach using artificial intelligence

Authors
 Hyunmin Ahn  ;  Na Eun Kim  ;  Jae Lim Chung  ;  Young Jun Kim  ;  Ikhyun Jun  ;  Tae-Im Kim  ;  Kyoung Yul Seo 
Citation
 FRONTIERS IN MEDICINE, Vol.9 : 934865, 2022-08 
Journal Title
FRONTIERS IN MEDICINE
Issue Date
2022-08
Keywords
Pentacam ; artificial intelligence ; corneal topography ; keratoconus ; machine learning ; screening test
Abstract
Background: Corneal topography is a clinically validated examination method for keratoconus. However, there is no clear guideline regarding patient selection for corneal topography. We developed and validated a novel artificial intelligence (AI) model to identify patients who would benefit from corneal topography based on basic ophthalmologic examinations, including a survey of visual impairment, best-corrected visual acuity (BCVA) measurement, intraocular pressure (IOP) measurement, and autokeratometry.

Methods: A total of five AI models (three individual models with fully connected neural network including the XGBoost, and the TabNet models, and two ensemble models with hard and soft voting methods) were trained and validated. We used three datasets collected from the records of 2,613 patients' basic ophthalmologic examinations from two institutions to train and validate the AI models. We trained the AI models using a dataset from a third medical institution to determine whether corneal topography was needed to detect keratoconus. Finally, prospective intra-validation dataset (internal test dataset) and extra-validation dataset from a different medical institution (external test dataset) were used to assess the performance of the AI models.

Results: The ensemble model with soft voting method outperformed all other AI models in sensitivity when predicting which patients needed corneal topography (90.5% in internal test dataset and 96.4% in external test dataset). In the error analysis, most of the predicting error occurred within the range of the subclinical keratoconus and the suspicious D-score in the Belin-Ambrósio enhanced ectasia display. In the feature importance analysis, out of 18 features, IOP was the highest ranked feature when comparing the average value of the relative attributions of three individual AI models, followed by the difference in the value of mean corneal power.

Conclusion: An AI model using the results of basic ophthalmologic examination has the potential to recommend corneal topography for keratoconus. In this AI algorithm, IOP and the difference between the two eyes, which may be undervalued clinical information, were important factors in the success of the AI model, and may be worth further reviewing in research and clinical practice for keratoconus screening.
Files in This Item:
T202204611.pdf Download
DOI
10.3389/fmed.2022.934865
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Ophthalmology (안과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Tae-Im(김태임) ORCID logo https://orcid.org/0000-0001-6414-3842
Seo, Kyoung Yul(서경률) ORCID logo https://orcid.org/0000-0002-9855-1980
Ahn, Hyunmin(안현민)
Jun, Ik Hyun(전익현) ORCID logo https://orcid.org/0000-0002-2160-1679
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/191744
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