Soh Zhi-Da, Tan Mingrui, Lee Zann, Yu Marco, Thakur Sahil, Lavanya Raghavan, Nongpiur Monisha Esther, Xu Xinxing, Koh Victor, Aung Tin, Liu Yong, Cheng Ching-Yu
Ocular Epidemiology Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore.
Br J Ophthalmol. 2025 Mar 20;109(4):497-503. doi: 10.1136/bjo-2024-325602.
BACKGROUND/ AIMS: The lack of context for anterior segment optical coherence tomography (ASOCT) measurements impedes its clinical utility. We established the normative distribution of anterior chamber depth (ACD), area (ACA) and width (ACW) and lens vault (LV), and applied percentile cut-offs to detect primary angle closure disease (PACD; ≥180° posterior trabecular meshwork occluded).
We included subjects from the Singapore Chinese Eye Study with ASOCT scans. Eyes with ocular surgery or laser procedures, and ocular trauma were excluded. A deep-learning algorithm was used to obtain Visante ASOCT (Carl Zeiss Meditec, USA) measurements. Normative distribution was established using 80% of eyes with open angles. Multivariable logistic regression was performed on 80% open and 80% angle closure eyes. Diagnostic performance was evaluated using 20% open and 20% angle closure eyes.
We included 2157 eyes (1853 open angles; 304 angle closure) for analysis. ACD, ACA and ACW decreased with age and were smaller in females, and vice versa for LV (all p<0.022). ACD 20th percentile and LV 85th percentile had a balanced accuracy of 84.4% and 84.2% in detecting PACD, respectively. When combined, ACD 20th and LV 85th percentile had 88.68% sensitivity and 88.85% specificity in detecting PACD as compared with a multivariable regression model (ACA, angle opening distance, LV, iris area) with 88.33% sensitivity and 83.75% specificity.
Anterior chamber parameters varied with age and gender. The ACD 20th and LV 85th percentile values may be used in silos or in combination to detect PACD in the absence of more sophisticated classification algorithms.
背景/目的：眼前节光学相干断层扫描（ASOCT）测量缺乏背景信息，这阻碍了其临床应用。我们建立了前房深度（ACD）、面积（ACA）、宽度（ACW）和晶状体拱顶（LV）的正常分布，并应用百分位数截断值来检测原发性房角关闭疾病（PACD；后小梁网闭塞≥180°）。
我们纳入了新加坡华人眼科研究中进行了ASOCT扫描的受试者。排除有眼部手术或激光治疗史以及眼外伤的眼睛。使用深度学习算法获取Visante ASOCT（美国卡尔蔡司医疗技术公司）测量值。利用80%房角开放的眼睛建立正常分布。对80%房角开放和80%房角关闭的眼睛进行多变量逻辑回归分析。使用20%房角开放和20%房角关闭的眼睛评估诊断性能。
我们纳入了2157只眼睛（1853只房角开放；304只房角关闭）进行分析。ACD、ACA和ACW随年龄增长而减小，女性更小，LV则相反（所有p<0.022）。ACD第20百分位数和LV第85百分位数在检测PACD时的平衡准确率分别为84.4%和84.2%。联合使用时，ACD第20百分位数和LV第85百分位数在检测PACD时的灵敏度为88.68%，特异度为88.85%，而多变量回归模型（ACA、房角开放距离、LV、虹膜面积）的灵敏度为88.33%，特异度为83.75%。
前房参数随年龄和性别而变化。在没有更复杂分类算法的情况下，ACD第20百分位数和LV第85百分位数的值可单独或联合用于检测PACD。
