Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery

Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery

Abstract This study aims to propose a generative deep learning model (GDLM) based on a variational autoencoder that predicts macular optical coherence tomography (OCT) images following full-thickness macular hole (FTMH) surgery and evaluate its clinical accuracy. Preoperative and 6-month postoperati...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Han Jo Kwon, Jun Heo, Su Hwan Park, Sung Who Park, Iksoo Byon
Format: Artykuł
Język:English
Wydane: Nature Portfolio 2024-03-01
Seria:Scientific Reports
Hasła przedmiotowe:
Deep learning
Generative deep learning model
Macular hole
Optical coherence tomography
Variational autoencoder
Dostęp online:https://doi.org/10.1038/s41598-024-57562-5

Internet

https://doi.org/10.1038/s41598-024-57562-5

Podobne zapisy