ArticleOphthalmology science2026
Artificial Intelligence-Derived Biomechanical Index for Early Corneal Ectasia Detection: Advancing Beyond Tomography.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Nanomaterial-Enhanced Corneal Cross-Linking: Engineering Strategies for Transforming Keratoconus Management.Pharmaceutics · 2026Review
- Reply.Ophthalmology science · 2026Article
- Re: Abdelmotaal et al: Artificial Intelligence-Derived Biomechanical Index for Early Corneal Ectasia Detection: Advancing Beyond Tomography.Ophthalmology science · 2026Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop and evaluate a novel artificial intelligence (AI)-derived metric for detection of early ectasia (E) by leveraging spatiotemporal biomechanical data derived from dynamic cornea videos only. Design: Multicenter cross-sectional case-control retrospective study. Participants: A total of 451 eyes from 451 patients from 2 centers with both Scheimpflug tomography and dynamic corneal deformation examinations. Methods: The training data set included 167 normal (N) patients with stable eyes postlaser vision correction, 83 NT eyes with VAE (VAE-NT), and better eyes from 64 patients with bilateral keratoconus (ie, mild KC [MKC]). The external validation data set included 68, 33, and 36 patients from the N, VAE-NT, and MKC groups, respectively. Extensive spatial and temporal pixel-level analysis of corneal displacement, stromal gray scale changes, and thickness variations from Corvis ST video frames was performed to identify a novel risk score with best detection of early E (combined VAE-NT and MKC). Findings were further validated using various approaches based on the external data set. Main Outcome Measures: Area under the receiver operating characteristics curve (AUC), accuracy, specificity, and sensitivity for detecting early E. Results: The novel AI-derived index achieved an AUC of 0.995 with accuracy, sensitivity, and specificity of 96%, 97%, and 96%, respectively, outperforming all Scheimpflug tomographic and dynamic deformation combined indices such as Belin/Ambrósio Enhanced Ectasia Display, Corvis Biomechanical Index (CBI), and Tomographic and Biomechanical Index (TBI). Conclusions: The AI-derived index based on spatiotemporal corneal biomechanical data outperformed existing instrument's topographic, tomographic, and biomechanical indices, in detecting E. This novel metric offers a clinically meaningful enhancement in early diagnosis and management of corneal E disease. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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