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dc.contributor.authorNakayama, Luis F.
dc.contributor.authorZago Ribeiro, Lucas
dc.contributor.authorde Oliveira, Juliana A. E.
dc.contributor.authorde Matos, João C. R. G.
dc.contributor.authorMitchell, William G.
dc.contributor.authorMalerbi, Fernando K.
dc.contributor.authorCeli, Leo A.
dc.contributor.authorRegatieri, Caio V. S.
dc.date.accessioned2023-09-27T18:25:04Z
dc.date.available2023-09-27T18:25:04Z
dc.date.issued2023-08-21
dc.identifier.urihttps://hdl.handle.net/1721.1/152267
dc.description.abstractAbstract Purpose In supervised Machine Learning algorithms, labels and reports are important in model development. To provide a normality assessment, the OCT has an in-built normative database that provides a color base scale from the measurement database comparison. This article aims to evaluate and compare normative databases of different OCT machines, analyzing patient demographic, contrast inclusion and exclusion criteria, diversity index, and statistical approach to assess their fairness and generalizability. Methods Data were retrieved from Cirrus, Avanti, Spectralis, and Triton’s FDA-approval and equipment manual. The following variables were compared: number of eyes and patients, inclusion and exclusion criteria, statistical approach, sex, race and ethnicity, age, participant country, and diversity index. Results Avanti OCT has the largest normative database (640 eyes). In every database, the inclusion and exclusion criteria were similar, including adult patients and excluding pathological eyes. Spectralis has the largest White (79.7%) proportionately representation, Cirrus has the largest Asian (24%), and Triton has the largest Black (22%) patient representation. In all databases, the statistical analysis applied was Regression models. The sex diversity index is similar in all datasets, and comparable to the ten most populous contries. Avanti dataset has the highest diversity index in terms of race, followed by Cirrus, Triton, and Spectralis. Conclusion In all analyzed databases, the data framework is static, with limited upgrade options and lacking normative databases for new modules. As a result, caution in OCT normality interpretation is warranted. To address these limitations, there is a need for more diverse, representative, and open-access datasets that take into account patient demographics, especially considering the development of supervised Machine Learning algorithms in healthcare.en_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofhttps://doi.org/10.1186/s40942-023-00459-8en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceBioMed Centralen_US
dc.titleFairness and generalizability of OCT normative databases: a comparative analysisen_US
dc.typeArticleen_US
dc.identifier.citationInternational Journal of Retina and Vitreous. 2023 Aug 21;9(1):48en_US
dc.contributor.departmentHarvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-08-27T03:12:04Z
dc.language.rfc3066en
dc.rights.holderBrazilian Retina and Vitreous Society
dspace.date.submission2023-08-27T03:12:04Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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