Validation of a deep learning model for the automated detection and quantification of cystoid macular oedema on optical coherence tomography in patients with retinitis pigmentosa.
Journal:
Acta ophthalmologica
Published Date:
May 21, 2025
Abstract
PURPOSE: Accurate assessment of cystoid macular oedema (CMO) in patients with retinitis pigmentosa (RP) on spectral-domain optical coherence tomography (SD-OCT) is crucial for tracking disease progression and may serve as a therapeutic endpoint. Manual CMO segmentation is labour-intensive and prone to variability, making artificial intelligence (AI) an appealing solution to improve accuracy and efficiency. This study aimed to validate a deep learning (DL) model for automated CMO detection and quantification on SD-OCT scans in patients with RP.
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