Deep Learning Using Preoperative Optical Coherence Tomography Images to Predict Visual Acuity Following Surgery for Idiopathic Full-Thickness Macular Holes

Deep Learning Using Preoperative Optical Coherence Tomography Images to Predict Visual Acuity Following Surgery for Idiopathic Full-Thickness Macular Holes

This study presents a fully automated image informatics framework. The framework is combined with a deep learning (DL) approach to automatically predict visual acuity outcomes for people undergoing surgery for idiopathic full-thickness macular holes using 3D spectral-domain optical coherence tomogra...

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Bibliographic Details
Main Authors: Burak Kucukgoz, Muhammed Mutlu Yapici, Declan C. Murphy, Emma Spowart, David H. Steel, Boguslaw Obara
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Image analysis
machine learning
optical coherence tomography
visual acuity measurement
Online Access:https://ieeexplore.ieee.org/document/10445126/

Internet

https://ieeexplore.ieee.org/document/10445126/

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