Sex-related difference in the retinal structure of young adults: a machine learning approach
PurposeTo compare the accuracy of machine learning (ML) algorithms to classify the sex of the participant from retinal thickness datasets in different retinal layers.MethodsThis cross-sectional study involved 26 male and 38 female subjects. Data were acquired using HRA + OCT Spectralis, and the thic...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1275308/full |
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| author | Flávia Monteiro Farias Flávia Monteiro Farias Railson Cruz Salomão Enzo Gabriel Rocha Santos Andrew Sousa Caires Gabriela Santos Alvarez Sampaio Alexandre Antônio Marques Rosa Marcelo Fernandes Costa Givago Silva Souza Givago Silva Souza |
| author_facet | Flávia Monteiro Farias Flávia Monteiro Farias Railson Cruz Salomão Enzo Gabriel Rocha Santos Andrew Sousa Caires Gabriela Santos Alvarez Sampaio Alexandre Antônio Marques Rosa Marcelo Fernandes Costa Givago Silva Souza Givago Silva Souza |
| author_sort | Flávia Monteiro Farias |
| collection | DOAJ |
| description | PurposeTo compare the accuracy of machine learning (ML) algorithms to classify the sex of the participant from retinal thickness datasets in different retinal layers.MethodsThis cross-sectional study involved 26 male and 38 female subjects. Data were acquired using HRA + OCT Spectralis, and the thickness and volume of 10 retinal layers were quantified. A total of 10 features were extracted from each retinal layer. The accuracy of various algorithms, including k-nearest-neighbor, support vector classifier, logistic regression, linear discriminant analysis, random forest, decision tree, and Gaussian Naïve Bayes, was quantified. A two-way ANOVA was conducted to assess the ML accuracy, considering both the classifier type and the retinal layer as factors.ResultsA comparison of the accuracies achieved by various algorithms in classifying participant sex revealed superior results in datasets related to total retinal thickness and the retinal nerve fiber layer. In these instances, no significant differences in algorithm performance were observed (p > 0.05). Conversely, in other layers, a decrease in classification accuracy was noted as the layer moved outward in the retina. Here, the random forest (RF) algorithm demonstrated superior performance compared to the others (p < 0.05).ConclusionThe current research highlights the distinctive potential of various retinal layers in sex classification. Different layers and ML algorithms yield distinct accuracies. The RF algorithm’s consistent superiority suggests its effectiveness in identifying sex-related features from a range of retinal layers. |
| first_indexed | 2024-03-08T23:28:37Z |
| format | Article |
| id | doaj.art-4a4e46697aaf4678b1cb755550e3a92e |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-03-08T23:28:37Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-4a4e46697aaf4678b1cb755550e3a92e2023-12-14T16:04:50ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-12-011010.3389/fmed.2023.12753081275308Sex-related difference in the retinal structure of young adults: a machine learning approachFlávia Monteiro Farias0Flávia Monteiro Farias1Railson Cruz Salomão2Enzo Gabriel Rocha Santos3Andrew Sousa Caires4Gabriela Santos Alvarez Sampaio5Alexandre Antônio Marques Rosa6Marcelo Fernandes Costa7Givago Silva Souza8Givago Silva Souza9Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, BrazilNúcleo de Medicina Tropical, Universidade Federal do Pará, Belém, BrazilNúcleo de Medicina Tropical, Universidade Federal do Pará, Belém, BrazilInstituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, BrazilInstituto de Ciências Biológicas, Universidade Federal do Pará, Belém, BrazilInstituto de Ciências Biológicas, Universidade Federal do Pará, Belém, BrazilInstituto de Ciências da Saúde, Universidade Federal do Pará, Belém, BrazilDepartamento de Psicologia, Instituto de Psicologia, Universidade de São Paulo, São Paulo, BrazilInstituto de Ciências Biológicas, Universidade Federal do Pará, Belém, BrazilNúcleo de Medicina Tropical, Universidade Federal do Pará, Belém, BrazilPurposeTo compare the accuracy of machine learning (ML) algorithms to classify the sex of the participant from retinal thickness datasets in different retinal layers.MethodsThis cross-sectional study involved 26 male and 38 female subjects. Data were acquired using HRA + OCT Spectralis, and the thickness and volume of 10 retinal layers were quantified. A total of 10 features were extracted from each retinal layer. The accuracy of various algorithms, including k-nearest-neighbor, support vector classifier, logistic regression, linear discriminant analysis, random forest, decision tree, and Gaussian Naïve Bayes, was quantified. A two-way ANOVA was conducted to assess the ML accuracy, considering both the classifier type and the retinal layer as factors.ResultsA comparison of the accuracies achieved by various algorithms in classifying participant sex revealed superior results in datasets related to total retinal thickness and the retinal nerve fiber layer. In these instances, no significant differences in algorithm performance were observed (p > 0.05). Conversely, in other layers, a decrease in classification accuracy was noted as the layer moved outward in the retina. Here, the random forest (RF) algorithm demonstrated superior performance compared to the others (p < 0.05).ConclusionThe current research highlights the distinctive potential of various retinal layers in sex classification. Different layers and ML algorithms yield distinct accuracies. The RF algorithm’s consistent superiority suggests its effectiveness in identifying sex-related features from a range of retinal layers.https://www.frontiersin.org/articles/10.3389/fmed.2023.1275308/fullretinaretinal thicknessmaculamachine learningsex-related differences |
| spellingShingle | Flávia Monteiro Farias Flávia Monteiro Farias Railson Cruz Salomão Enzo Gabriel Rocha Santos Andrew Sousa Caires Gabriela Santos Alvarez Sampaio Alexandre Antônio Marques Rosa Marcelo Fernandes Costa Givago Silva Souza Givago Silva Souza Sex-related difference in the retinal structure of young adults: a machine learning approach Frontiers in Medicine retina retinal thickness macula machine learning sex-related differences |
| title | Sex-related difference in the retinal structure of young adults: a machine learning approach |
| title_full | Sex-related difference in the retinal structure of young adults: a machine learning approach |
| title_fullStr | Sex-related difference in the retinal structure of young adults: a machine learning approach |
| title_full_unstemmed | Sex-related difference in the retinal structure of young adults: a machine learning approach |
| title_short | Sex-related difference in the retinal structure of young adults: a machine learning approach |
| title_sort | sex related difference in the retinal structure of young adults a machine learning approach |
| topic | retina retinal thickness macula machine learning sex-related differences |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1275308/full |
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