Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms

PURPOSE: We formulated and tested ensemble learning models to classify axial length (AXL) from choroidal thickness (CT) as indicated on fovea-centered, 2D single optical coherence tomography (OCT) images. DESIGN: Retrospective cross-sectional study. PARTICIPANTS: We analyzed 710 OCT images from 355...

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Detalles Bibliográficos
Autores principales: Lu, Hao-Chun, Chen, Hsin-Yi, Huang, Chien-Jung, Chu, Pao-Hsien, Wu, Lung-Sheng, Tsai, Chia-Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273745/
https://www.ncbi.nlm.nih.gov/pubmed/35836947
http://dx.doi.org/10.3389/fmed.2022.850284

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273745/
https://www.ncbi.nlm.nih.gov/pubmed/35836947
http://dx.doi.org/10.3389/fmed.2022.850284

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