Zhou Wen-Da, Zhao Han-Qing, Geng Jia-Qi, Yang Yu-Hang, Dong Li, Zhang Rui-Heng, Wei Wen-Bin, Shao Lei
Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
EVision Technology (Beijing) Co. LTD, Beijing, China.
Transl Vis Sci Technol. 2025 Jun 2;14(6):9. doi: 10.1167/tvst.14.6.9.
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.
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.
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.
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.
This study validates AI-based SFCT measurement in a large elderly cohort, enhancing diagnostic accuracy and bridging research with practice.
评估在老年人群中使用增强深度成像光谱域光学相干断层扫描技术手动测量和自动测量黄斑中心凹下脉络膜厚度（SFCT）之间的一致性和相关性，并研究影响测量差异的因素。
基于北京眼病研究，使用海德堡眼探险家软件手动测量SFCT，并通过基于TransUNet的深度学习模型自动测量。使用布兰德-奥特曼图、组内相关系数（ICC）和皮尔逊相关系数评估手动和自动SFCT测量之间的一致性。
在2896名参与者中，SFCT的自动测量和手动测量显示出很强的相关性（ICC = 0.971；95%置信区间[CI]，0.969 - 0.973；皮尔逊系数 = 0.974，P < 0.001）。亚组分析显示，年龄≥60岁的参与者（ICC = 0.954，皮尔逊系数 = 0.974）、年龄<60岁的参与者（ICC = 0.971；皮尔逊系数 = 0.953）、眼轴长度≥23 mm的参与者（ICC = 0.969；皮尔逊系数 = 0.974）以及眼轴长度<23 mm的参与者（ICC = 0.959；皮尔逊系数 = 0.963）之间的相关性同样很高。与SFCT≥300 µm的参与者（ICC = 0.867；皮尔逊系数 = 0.868）相比，SFCT<300 µm的参与者显示出更高的一致性（ICC = 0.942；皮尔逊系数 = 0.944）。在所有亚组中均观察到显著的固定偏差和比例偏差（P < 0.001），手动测量值始终低于自动测量值。
尽管存在系统偏差，但在大量老年人群中，自动SFCT测量与手动测量显示出极好的一致性和很强的相关性。这些发现支持了人工智能辅助SFCT测量在临床环境中的潜在应用价值。
本研究在一个大型老年队列中验证了基于人工智能的SFCT测量，提高了诊断准确性，并将研究与实践联系起来。
