2022•American Journal of OphthalmologyOpen access
Optimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Data
Renato Ambrósio, Aydano Pamponet Machado, Edileuza Virginio Leão, João Marcelo A G Lyra, Marcella Quaresma Salomão, Louise G. Pellegrino Esporcatte, João Batista R. da Fonseca Filho, Erica Ferreira-Meneses, Nelson Sena, Jorge Selem Haddad, Alexandre Batista da Costa Neto, Gildasio Castelo de Almeida, Cynthia J. Roberts, Ahmed Elsheikh, Riccardo Vinciguerra, Paolo Vinciguerra, Jens Bühren, Thomas Kohnen, Guy M. Kezirian, Farhad Hafezi, Nikki L. Hafezi, Emílio A. Torres-Netto, Nan‐Ji Lu, David Sung Yong Kang, Omid Kermani, Shizuka Koh, Prema Padmanabhan, Suphi Taneri, William Trattler, Luca Gualdi, José M Salgado-Borges, Fernando Faria-Correia, Elias Flockerzi, Berthold Seitz, Vishal Jhanji, Tommy C.Y. Chan, Pedro Manuel Baptista, Dan Z. Reinstein, Timothy J. Archer, Karolinne Maia Rocha, George Oral Waring IV, Ronald R. Krueger, William Joseph Dupps, Ramin Khoramnia, Hassan Hashemi, Soheila Asgari, Hamed Momeni‐Moghaddam, Siamak Zarei‐Ghanavati, Rohit Shetty, Pooja Khamar, Michael Wellington Belin, Bernardo Teixeira Lopes
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.