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). Methods 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. Results 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. Conclusion 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. Data are available on reasonable request. The data included in this study are not publicly available due to patient privacy and the data are meant for research purposes only. On reasonable request, de-identified data used in this study may be made available for academic purpose by the Singapore Eye Research Institute (SERI), subjected to approval by the local institutional review board. Data request can be sent to the Data Access Committee at SERI via seri@seri.com.sg. Any data that can be shared will be released via a Research Collaboration Agreement (RCA) for non-commercial research purpose.

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用于房角闭合评估的基于深度学习的前房尺寸规范数据库：新加坡华人眼研究


背景/目的 眼前节光学相干断层扫描 （ASOCT） 测量缺乏背景阻碍了其临床应用。我们建立了前房深度 （ACD） 、面积 （ACA） 和宽度 （ACW） 以及晶状体穹窿 （LV） 的标准分布，并应用百分位数临界值来检测原发性房角闭合病 （PACD;≥180° 后小梁网闭塞）。方法 我们纳入了新加坡华人眼研究的受试者和 ASOCT 扫描。排除了眼科手术或激光手术的眼睛和眼外伤。使用深度学习算法获得 Visante ASOCT（Carl Zeiss Meditec，美国）测量值。使用 80% 的开角眼睛建立规范分布。对 80% 睁开和 80% 闭角眼进行多变量 logistic 回归。使用 20% 睁开眼和 20% 闭角眼评估诊断性能。结果 我们纳入了 2157 只眼睛 （1853 个开角;304 个闭角） 进行分析。ACD 、 ACA 和 ACW 随年龄增长而降低，女性较小，LV 反之亦然 （均 p<0.022）。ACD 第 20 个百分位数和 LV 第 85 个百分位数在检测 PACD 方面的平衡准确率分别为 84.4% 和 84.2%。当联合使用时，ACD 第 20 和 LV 第 85 个百分位数检测 PACD 的灵敏度为 88.68%，特异性为 88.85%，而多变量回归模型 （ACA、开角距离、LV、虹膜面积） 的灵敏度为 88.33%，特异性为 83.75%。结论 前房参数随年龄和性别而变化。ACD 第 20 个和 LV 第 85 个百分位值可以孤岛使用，也可以在没有更复杂的分类算法的情况下组合使用来检测 PACD。数据可应合理要求提供。 由于患者隐私，本研究中包含的数据不公开，数据仅用于研究目的。根据合理要求，新加坡眼科研究所 （SERI） 可能会出于学术目的提供本研究中使用的去标识化数据，但须经当地机构审查委员会批准。数据请求可以通过 seri@seri.com.sg 发送给 SERI 的数据访问委员会。任何可以共享的数据都将通过研究合作协议 （RCA） 发布，用于非商业研究目的。