Cheng Lu, Hong Jiaming, Chen Hailiu, Ling Yunlan, Lin Shufen, Huang Jing, Wu Ethan, Li Yangyunhui, Lin Haotian, Liu Shaopeng, Huang Jingjing
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
BMJ Open Ophthalmol. 2025 May 22;10(1):e002114. doi: 10.1136/bmjophth-2024-002114.
This study aims to develop ultrasound biomicroscopy (UBM)-based artificial intelligence (AI) models for preoperative differentiation of acute angle closure (AAC) with or without zonulopathy and to compare their comprehensive diagnostic performance against ophthalmologists as a cross-sectional study.
Three AI models were developed to differentiate AAC with or without zonular laxity or lens subluxation using UBM images and ocular parameters. Their diagnostic performances were analysed, with the best-performing model then compared with two diagnostic methods used by ophthalmologists (logistic regression and UBM image analysis). Additionally, a robustness validation dataset, including images from UBM and anterior segment optical coherence tomography (AS-OCT), was used to validate the robustness of the best-performing AI model.
A total of 537 eyes were included in this study. The best-performing AI model was image-based and achieved a macro-area under the curve (AUC) of 0.9046 with a diagnostic processing time of 0.03 s per image in differentiating AAC with or without zonulopathy. The manually calculated multinomial logistic regression model achieved a macro-AUC of 0.9373, requiring 1200.00 s per analysis. UBM image analysis achieved a mean accuracy and processing time of 64.17% and 20.13 s, respectively, per image. Robustness validation of the image-based AI model showed an accuracy of 66.67% and 61.11% for UBM and AS-OCT images.
AI models and ophthalmologists effectively differentiated AAC with or without zonulopathy. However, when evaluated in terms of both accuracy and efficiency, the AI model showed superior comprehensive diagnostic performance, demonstrating high clinical applicability for preoperative diagnosis.
本研究旨在开发基于超声生物显微镜（UBM）的人工智能（AI）模型，用于术前鉴别有无晶状体悬韧带病变的急性闭角型青光眼（AAC），并作为一项横断面研究，将其综合诊断性能与眼科医生的进行比较。
开发了三种AI模型，利用UBM图像和眼部参数鉴别有无晶状体悬韧带松弛或晶状体半脱位的AAC。分析了它们的诊断性能，然后将性能最佳的模型与眼科医生使用的两种诊断方法（逻辑回归和UBM图像分析）进行比较。此外，使用一个稳健性验证数据集（包括来自UBM和眼前节光学相干断层扫描（AS-OCT）的图像）来验证性能最佳的AI模型的稳健性。
本研究共纳入537只眼。性能最佳的AI模型是基于图像的，在鉴别有无晶状体悬韧带病变的AAC时，曲线下宏面积（AUC）为0.9046，每张图像的诊断处理时间为0.03秒。手动计算的多项逻辑回归模型的宏AUC为0.9373，每次分析需要1200.00秒。UBM图像分析的平均准确率和处理时间分别为每张图像64.17%和20.13秒。基于图像的AI模型的稳健性验证显示，对于UBM和AS-OCT图像，准确率分别为66.67%和61.11%。
AI模型和眼科医生都能有效鉴别有无晶状体悬韧带病变的AAC。然而，在准确性和效率方面进行评估时，AI模型显示出卓越的综合诊断性能，对术前诊断具有很高的临床适用性。
