Comparison of AI-Automated and Manual Subfoveal Choroidal Thickness Measurements in an Elderly Population Using Optical Coherence Tomography.

Purpose: To evaluate the agreement and correlation between manual and automated measurements of subfoveal choroidal thickness (SFCT) using enhanced depth imaging spectral-domain optical coherence tomography in an elderly population and to investigate the factors influencing measurement discrepancies.

Methods: Based on the Beijing Eye Study, SFCT was measured manually using Heidelberg Eye Explorer software and automatically via a TransUNet-based deep learning model. Agreement between manual and automated SFCT measurements was assessed using Bland-Altman plots, intraclass correlation coefficients (ICC), and Pearson correlation coefficients.

Results: Among 2896 participants, automated and manual measurements of SFCT demonstrated strong correlation (ICC = 0.971; 95% confidence interval [CI], 0.969-0.973; Pearson = 0.974, P < 0.001). Subgroup analyses showed similarly high correlation across participants aged ≥60 years (ICC = 0.954, Pearson = 0.974), aged <60 years (ICC = 0.971; Pearson = 0.953), with axial length ≥23 mm (ICC = 0.969; Pearson = 0.974), and axial length <23 mm (ICC = 0.959; Pearson = 0.963). Participants with SFCT <300 µm showed higher consistency (ICC = 0.942; Pearson = 0.944) compared to those with SFCT ≥300 µm (ICC = 0.867; Pearson = 0.868). Significant fixed and proportional biases were observed in all subgroups (P < 0.001), with manual measurements consistently lower than automated values.

Conclusions: Despite the presence of systematic biases, automated SFCT measurements showed excellent consistency and strong correlation with manual measurements across a large elderly population. These findings support the potential utility of AI-assisted SFCT measurement in clinical settings.

Translational Relevance: This study validates AI-based SFCT measurement in a large elderly cohort, enhancing diagnostic accuracy and bridging research with practice.