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

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

doi:10.1016/j.ajo.2022.12.016

Public

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

Published in American Journal of Ophthalmology • Dec 19, 2022

Authors:

Renato Ambrósio

Aydano Pamponet Machado

Edileuza Virginio Leão

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...

Subject

Scheimpflug principle

Ectasia

Receiver operating characteristic

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