Optimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Data.

R Jr, Ambrósio; AP, Machado; E, Leão; JMG, Lyra; MQ, Salomão; LGP, Esporcatte; Fonseca Filho JBR, da; E, Ferreira-Meneses; NB Jr, Sena; JS, Haddad; Neto A, Costa; Almeida GC Jr, de; CJ, Roberts; A, Elsheikh; R, Vinciguerra; P, Vinciguerra; J, Bühren; T, Kohnen; GM, Kezirian; F, Hafezi; NL, Hafezi; EA, Torres-Netto; N, Lu; DSY, Kang; O, Kermani; S, Koh; P, Padmanabhan; S, Taneri; W, Trattler; L, Gualdi; J, Salgado-Borges; F, Faria-Correia; E, Flockerzi; B, Seitz; V, Jhanji; TCY, Chan; PM, Baptista; DZ, Reinstein; TJ, Archer; KM, Rocha; GO 4th, Waring; RR, Krueger; WJ, Dupps; R, Khoramnia; H, Hashemi; S, Asgari; H, Momeni-Moghaddam; S, Zarei-Ghanavati; R, Shetty; P, Khamar; MW, Belin; BT, Lopes

doi:10.1016/j.ajo.2022.12.016

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Optimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Data.

Shared by NobleBlocks on Dec 19, 2022 • 12:00 AM UTC

Authors:

Ambrósio R Jr

Machado AP

Leão E

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 ...

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