Human versus Artificial Intelligence: Validation of a Deep Learning Model for retinal layer and fluid segmentation in Optical Coherence Tomography Images from patients with Age-Related Macular Degeneration

Abstract

Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold standard method of manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular disease (AMD) were included. A quantitative analysis of retinal thickness and fluid area was performed and the discrepancy between the methods was investigated. The results showed a moderate to strong correlation between the metrics extracted by both software, in all groups, and an overall near-perfect area overlap was observed, except for the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between the methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.