Optimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Data.
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Ambrósio Renato, Machado Aydano P, Leão Edileuza, Lyra João Marcelo G, Salomão Marcella Q, Esporcatte Louise G Pellegrino, da Fonseca Filho João B R, Ferreira-Meneses Erica, Sena Nelson B, Haddad Jorge S, Costa Neto Alexandre, de Almeida Gildasio Castelo, Roberts Cynthia J, Elsheikh Ahmed, Vinciguerra Riccardo, Vinciguerra Paolo, Bühren Jens, Kohnen Thomas, Kezirian Guy M, Hafezi Farhad, Hafezi Nikki L, Torres-Netto Emilio A, Lu Nanji, Kang David Sung Yong, Kermani Omid, Koh Shizuka, Padmanabhan Prema, Taneri Suphi, Trattler William, Gualdi Luca, Salgado-Borges José, Faria-Correia Fernando, Flockerzi Elias, Seitz Berthold, Jhanji Vishal, Chan Tommy C Y, Baptista Pedro Manuel, Reinstein Dan Z, Archer Timothy J, Rocha Karolinne M, Waring George O, Krueger Ronald R, Dupps William J, Khoramnia Ramin, Hashemi Hassan, Asgari Soheila, Momeni-Moghaddam Hamed, Zarei-Ghanavati Siamak, Shetty Rohit, Khamar Pooja, Belin Michael W, Lopes Bernardo T
Аннотация
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.