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Fairness and generalizability of OCT normative databases: a comparative analysis

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 compar...

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Autores principales: Nakayama, Luis Filipe, Zago Ribeiro, Lucas, de Oliveira, Juliana Angelica Estevão, de Matos, João Carlos Ramos Gonçalves, Mitchell, William Greig, Malerbi, Fernando Korn, Celi, Leo Anthony, Regatieri, Caio Vinicius Saito
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440930/
https://www.ncbi.nlm.nih.gov/pubmed/37605208
http://dx.doi.org/10.1186/s40942-023-00459-8
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author Nakayama, Luis Filipe
Zago Ribeiro, Lucas
de Oliveira, Juliana Angelica Estevão
de Matos, João Carlos Ramos Gonçalves
Mitchell, William Greig
Malerbi, Fernando Korn
Celi, Leo Anthony
Regatieri, Caio Vinicius Saito
author_facet Nakayama, Luis Filipe
Zago Ribeiro, Lucas
de Oliveira, Juliana Angelica Estevão
de Matos, João Carlos Ramos Gonçalves
Mitchell, William Greig
Malerbi, Fernando Korn
Celi, Leo Anthony
Regatieri, Caio Vinicius Saito
author_sort Nakayama, Luis Filipe
collection PubMed
description 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.
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spelling pubmed-104409302023-08-22 Fairness and generalizability of OCT normative databases: a comparative analysis Nakayama, Luis Filipe Zago Ribeiro, Lucas de Oliveira, Juliana Angelica Estevão de Matos, João Carlos Ramos Gonçalves Mitchell, William Greig Malerbi, Fernando Korn Celi, Leo Anthony Regatieri, Caio Vinicius Saito Int J Retina Vitreous Original Article 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. BioMed Central 2023-08-21 /pmc/articles/PMC10440930/ /pubmed/37605208 http://dx.doi.org/10.1186/s40942-023-00459-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Original Article
Nakayama, Luis Filipe
Zago Ribeiro, Lucas
de Oliveira, Juliana Angelica Estevão
de Matos, João Carlos Ramos Gonçalves
Mitchell, William Greig
Malerbi, Fernando Korn
Celi, Leo Anthony
Regatieri, Caio Vinicius Saito
Fairness and generalizability of OCT normative databases: a comparative analysis
title Fairness and generalizability of OCT normative databases: a comparative analysis
title_full Fairness and generalizability of OCT normative databases: a comparative analysis
title_fullStr Fairness and generalizability of OCT normative databases: a comparative analysis
title_full_unstemmed Fairness and generalizability of OCT normative databases: a comparative analysis
title_short Fairness and generalizability of OCT normative databases: a comparative analysis
title_sort fairness and generalizability of oct normative databases: a comparative analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440930/
https://www.ncbi.nlm.nih.gov/pubmed/37605208
http://dx.doi.org/10.1186/s40942-023-00459-8
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