Development and Validation of a Deep Learning Model for Macular Hole Staging from Optical Coherence Tomography
Version 1Objective: To explore the use of artificial intelligence (AI) deep learning algorithms to construct an automated identification model for macular hole (MH) staging and intelligent assisted diagnosis and treatment system based on fundus optical coherence tomography (OCT) images. Method: Based on the image data of macular hole diagnosed by OCT examination at Sichuan Eye Hospital from July 2019 to October 2024, combined with manual and procedural screening methods, images with annotation errors and
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Objective: To explore the use of artificial intelligence (AI) deep learning algorithms to construct an automated identification model for macular hole (MH) staging and intelligent assisted diagnosis and treatment system based on fundus optical coherence tomography (OCT) images. Method: Based on the image data of macular hole diagnosed by OCT examination at Sichuan Eye Hospital from July 2019 to October 2024, combined with manual and procedural screening methods, images with annotation errors and