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Browsing by Author "Sena, Nelson B."

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  • Optimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Data
    Publication . 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.
    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.
    2023Journal article Open access Show more