Ambrósio, RenatoMachado, Aydano P.Leão, EdileuzaLyra, João Marcelo G.Salomão, Marcella Q.Esporcatte, Louise G. Pellegrinoda Fonseca Filho, João B.R.Ferreira-Meneses, EricaSena, Nelson B.Haddad, Jorge S.Costa Neto, Alexandrede Almeida, Gildasio CasteloRoberts, Cynthia J.Elsheikh, AhmedVinciguerra, RiccardoVinciguerra, PaoloBühren, JensKohnen, ThomasKezirian, Guy M.Hafezi, FarhadHafezi, Nikki L.Torres-Netto, Emilio A.Lu, NanjiKang, David Sung YongKermani, OmidKoh, ShizukaPadmanabhan, PremaTaneri, SuphiTrattler, WilliamGualdi, LucaSalgado-Borges, JoséFaria-Correia, FernandoFlockerzi, EliasSeitz, BertholdJhanji, VishalChan, Tommy C.Y.Baptista, Pedro ManuelReinstein, Dan Z.Archer, Timothy J.Rocha, Karolinne M.Waring, George O.Krueger, Ronald R.Dupps, William J.Khoramnia, RaminHashemi, HassanAsgari, SoheilaMomeni-Moghaddam, HamedZarei-Ghanavati, SiamakShetty, RohitKhamar, PoojaBelin, Michael W.Lopes, Bernardo T.2024-07-042024-07-042023Ambrósio R Jr, Machado AP, Leão E, et al. Optimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Data. Am J Ophthalmol. 2023;251:126-142. doi:10.1016/j.ajo.2022.12.0160002-93941879-1891http://hdl.handle.net/10400.16/2995Purpose: 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.engjournal article10.1016/j.ajo.2022.12.016