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

2023Journal article Open access
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dc.contributor.authorAmbrósio, Renato
dc.contributor.authorMachado, Aydano P.
dc.contributor.authorLeão, Edileuza
dc.contributor.authorLyra, João Marcelo G.
dc.contributor.authorSalomão, Marcella Q.
dc.contributor.authorEsporcatte, Louise G. Pellegrino
dc.contributor.authorda Fonseca Filho, João B.R.
dc.contributor.authorFerreira-Meneses, Erica
dc.contributor.authorSena, Nelson B.
dc.contributor.authorHaddad, Jorge S.
dc.contributor.authorCosta Neto, Alexandre
dc.contributor.authorde Almeida, Gildasio Castelo
dc.contributor.authorRoberts, Cynthia J.
dc.contributor.authorElsheikh, Ahmed
dc.contributor.authorVinciguerra, Riccardo
dc.contributor.authorVinciguerra, Paolo
dc.contributor.authorBühren, Jens
dc.contributor.authorKohnen, Thomas
dc.contributor.authorKezirian, Guy M.
dc.contributor.authorHafezi, Farhad
dc.contributor.authorHafezi, Nikki L.
dc.contributor.authorTorres-Netto, Emilio A.
dc.contributor.authorLu, Nanji
dc.contributor.authorKang, David Sung Yong
dc.contributor.authorKermani, Omid
dc.contributor.authorKoh, Shizuka
dc.contributor.authorPadmanabhan, Prema
dc.contributor.authorTaneri, Suphi
dc.contributor.authorTrattler, William
dc.contributor.authorGualdi, Luca
dc.contributor.authorSalgado-Borges, José
dc.contributor.authorFaria-Correia, Fernando
dc.contributor.authorFlockerzi, Elias
dc.contributor.authorSeitz, Berthold
dc.contributor.authorJhanji, Vishal
dc.contributor.authorChan, Tommy C.Y.
dc.contributor.authorBaptista, Pedro Manuel
dc.contributor.authorReinstein, Dan Z.
dc.contributor.authorArcher, Timothy J.
dc.contributor.authorRocha, Karolinne M.
dc.contributor.authorWaring, George O.
dc.contributor.authorKrueger, Ronald R.
dc.contributor.authorDupps, William J.
dc.contributor.authorKhoramnia, Ramin
dc.contributor.authorHashemi, Hassan
dc.contributor.authorAsgari, Soheila
dc.contributor.authorMomeni-Moghaddam, Hamed
dc.contributor.authorZarei-Ghanavati, Siamak
dc.contributor.authorShetty, Rohit
dc.contributor.authorKhamar, Pooja
dc.contributor.authorBelin, Michael W.
dc.contributor.authorLopes, Bernardo T.
dc.date.accessioned2024-07-04T11:04:33Z
dc.date.available2024-07-04T11:04:33Z
dc.date.issued2023
dc.description.abstractPurpose: 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAmbrósio 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.2022.12.016pt_PT
dc.identifier.doi10.1016/j.ajo.2022.12.016pt_PT
dc.identifier.issn0002-9394
dc.identifier.issn1879-1891
dc.identifier.urihttp://hdl.handle.net/10400.16/2995
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relation.publisherversionhttps://doi.org/10.1016/j.ajo.2022.12.016pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.titleOptimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceUnited States of Americapt_PT
oaire.citation.endPage142pt_PT
oaire.citation.issueElsevier Sciencept_PT
oaire.citation.startPage126pt_PT
oaire.citation.titleAmerican Journal of Ophthalmologypt_PT
oaire.citation.volume251pt_PT
person.familyNameBaptista
person.givenNamePedro Manuel
person.identifier.orcid0000-0001-8285-1084
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationaefa59ff-77f9-42ff-ac9b-88373f5c2ac8
relation.isAuthorOfPublication.latestForDiscoveryaefa59ff-77f9-42ff-ac9b-88373f5c2ac8

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