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Status: Bibliographieeintrag

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Verfasst von:Ambrósio, Renato [VerfasserIn]   i
 Machado, Aydano P. [VerfasserIn]   i
 Leão, Edileuza [VerfasserIn]   i
 Lyra, João Marcelo G. [VerfasserIn]   i
 Salomão, Marcella Q. [VerfasserIn]   i
 Esporcatte, Louise G. Pellegrino [VerfasserIn]   i
 da Fonseca Filho, João B. R. [VerfasserIn]   i
 Ferreira-Meneses, Erica [VerfasserIn]   i
 Sena, Nelson B. [VerfasserIn]   i
 Haddad, Jorge S. [VerfasserIn]   i
 Costa Neto, Alexandre [VerfasserIn]   i
 de Almeida, Gildasio Castelo [VerfasserIn]   i
 Roberts, Cynthia J. [VerfasserIn]   i
 Elsheikh, Ahmed [VerfasserIn]   i
 Vinciguerra, Riccardo [VerfasserIn]   i
 Vinciguerra, Paolo [VerfasserIn]   i
 Bühren, Jens [VerfasserIn]   i
 Kohnen, Thomas [VerfasserIn]   i
 Kezirian, Guy M. [VerfasserIn]   i
 Hafezi, Farhad [VerfasserIn]   i
 Hafezi, Nikki L. [VerfasserIn]   i
 Torres-Netto, Emilio A. [VerfasserIn]   i
 Lu, Nanji [VerfasserIn]   i
 Kang, David Sung Yong [VerfasserIn]   i
 Kermani, Omid [VerfasserIn]   i
 Koh, Shizuka [VerfasserIn]   i
 Padmanabhan, Prema [VerfasserIn]   i
 Taneri, Suphi [VerfasserIn]   i
 Trattler, William [VerfasserIn]   i
 Gualdi, Luca [VerfasserIn]   i
 Salgado-Borges, José [VerfasserIn]   i
 Faria-Correia, Fernando [VerfasserIn]   i
 Flockerzi, Elias [VerfasserIn]   i
 Seitz, Berthold [VerfasserIn]   i
 Jhanji, Vishal [VerfasserIn]   i
 Chan, Tommy C. Y. [VerfasserIn]   i
 Baptista, Pedro Manuel [VerfasserIn]   i
 Reinstein, Dan Z. [VerfasserIn]   i
 Archer, Timothy J. [VerfasserIn]   i
 Rocha, Karolinne M. [VerfasserIn]   i
 Waring, George O. [VerfasserIn]   i
 Krueger, Ronald R. [VerfasserIn]   i
 Dupps, William J. [VerfasserIn]   i
 Khoramnia, Ramin [VerfasserIn]   i
 Hashemi, Hassan [VerfasserIn]   i
 Asgari, Soheila [VerfasserIn]   i
 Momeni-Moghaddam, Hamed [VerfasserIn]   i
 Zarei-Ghanavati, Siamak [VerfasserIn]   i
 Shetty, Rohit [VerfasserIn]   i
 Khamar, Pooja [VerfasserIn]   i
 Belin, Michael W. [VerfasserIn]   i
 Lopes, Bernardo T. [VerfasserIn]   i
Titel:Optimized artificial intelligence for enhanced ectasia detection using scheimpflug-based corneal tomography and biomechanical data
Verf.angabe:Renato Ambrósio, Aydano P. Machado, Edileuza Leão, João Marcelo G. Lyra, Marcella Q. Salomão, Louise G. Pellegrino Esporcatte, João B.R. da Fonseca Filho, Erica Ferreira-Meneses, Nelson B. Sena, Jorge S. Haddad, Alexandre 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, Emilio A. Torres-Netto, Nanji Lu, David Sung Yong Kang, Omid Kermani, Shizuka Koh, Prema Padmanabhan, Suphi Taneri, William Trattler, Luca Gualdi, José 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 M. Rocha, George O. Waring, Ronald R. Krueger, William J. Dupps, Ramin Khoramnia, Hassan Hashemi, Soheila Asgari, Hamed Momeni-Moghaddam, Siamak Zarei-Ghanavati, Rohit Shetty, Pooja Khamar, Michael W. Belin, and Bernardo T. Lopes
E-Jahr:2023
Jahr:15 April 2023
Umfang:17 S.
Fussnoten:Online verfügbar 19 December 2022, Version des Artikels 15 April 2023 ; Gesehen am 11.08.2023
Titel Quelle:Enthalten in: American journal of ophthalmology
Ort Quelle:New York, NY : Elsevier Science, 1918
Jahr Quelle:2023
Band/Heft Quelle:251(2023) vom: Juli, Seite 126-142
ISSN Quelle:1879-1891
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.
DOI:doi:10.1016/j.ajo.2022.12.016
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1016/j.ajo.2022.12.016
 Volltext: https://www.sciencedirect.com/science/article/pii/S0002939422005062
 DOI: https://doi.org/10.1016/j.ajo.2022.12.016
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1855282305
Verknüpfungen:→ Zeitschrift

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