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Optimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Data
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Final Published Version
Affiliation
Department of Ophthalmology & Vision Science, University of ArizonaIssue Date
2023-07Metadata
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Elsevier Inc.Citation
Ambrósio Jr, R., Machado, A. P., Leão, E., Lyra, J. M. G., Salomão, M. Q., Esporcatte, L. G. P., ... & Lopes, B. T. (2023). Optimized artificial intelligence for enhanced ectasia detection using Scheimpflug-based corneal tomography and biomechanical data. American journal of ophthalmology, 251, 126-142.Rights
© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY License (https://creativecommons.org/licenses/by/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Purpose: To optimize artificial intelligence (AI) algorithms to integrate Scheimpflug-based corneal tomography and biomechanics to enhance ectasia detection. Design: Multicenter cross-sectional case-control retrospective study. Methods: A total of 3886 unoperated eyes from 3412 patients had Pentacam and Corvis ST (Oculus Optikgeräte GmbH) examinations. The database included 1 eye randomly selected from 1680 normal patients (N) and from 1181 “bilateral” keratoconus (KC) patients, along with 551 normal topography eyes from patients with very asymmetric ectasia (VAE-NT), and their 474 unoperated ectatic (VAE-E) eyes. The current TBIv1 (tomographic-biomechanical index) was tested, and an optimized AI algorithm was developed for augmenting accuracy. Results: The area under the receiver operating characteristic curve (AUC) of the TBIv1 for discriminating clinical ectasia (KC and VAE-E) was 0.999 (98.5% sensitivity; 98.6% specificity [cutoff: 0.5]), and for VAE-NT, 0.899 (76% sensitivity; 89.1% specificity [cutoff: 0.29]). A novel random forest algorithm (TBIv2), developed with 18 features in 156 trees using 10-fold cross-validation, had a significantly higher AUC (0.945; DeLong, P <.0001) for detecting VAE-NT (84.4% sensitivity and 90.1% specificity; cutoff: 0.43; DeLong, P <.0001) and a similar AUC for clinical ectasia (0.999; DeLong, P =.818; 98.7% sensitivity; 99.2% specificity [cutoff: 0.8]). Considering all cases, the TBIv2 had a higher AUC (0.985) than TBIv1 (0.974; DeLong, P <.0001). Conclusions: AI optimization to integrate Scheimpflug-based corneal tomography and biomechanical assessments augments accuracy for ectasia detection, characterizing ectasia susceptibility in the diverse VAE-NT group. Some patients with VAE may have true unilateral ectasia. Machine learning considering additional data, including epithelial thickness or other parameters from multimodal refractive imaging, will continuously enhance accuracy. NOTE: Publication of this article is sponsored by the American Ophthalmological Society. © 2022Note
Open access articleISSN
0002-9394PubMed ID
36549584Version
Final Published VersionScopus Count
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Except where otherwise noted, this item's license is described as © 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY License (https://creativecommons.org/licenses/by/4.0/).