Cong Yuyu, Jiang Weiyan, Dong Zehua, Zhu Jian, Yang Yuanhao, Wang Yujin, Deng Qian, Yan Yulin, Mao Jiewen, Shi Xiaoshuo, Pan Jiali, Yang Zixian, Wang Yingli, Fang Juntao, Zheng Biqing, Yang Yanning
The Department of Ophthalmology, Renmin Hospital of Wuhan University, Wuhan 430061, China.
The Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430061, China.
Bioengineering (Basel). 2025 Apr 14;12(4):415. doi: 10.3390/bioengineering12040415.
Primary angle-closure glaucoma (PACG), characterized by angle closure (AC) with insidious and irreversible progression, requires precise assessment of AC mechanisms for accurate diagnosis and treatment. This study developed an artificial intelligence system, ACM-Assessor, to evaluate AC mechanisms in ultrasound biomicroscopy (UBM) images. A dataset of 8482 UBM images from 1160 patients was retrospectively collected. ACM-Assessor comprises models for pixel-to-physical spacing conversion, anterior chamber angle boundary segmentation, and scleral spur localization, along with three binary classification models to assess pupillary block (PB), thick peripheral iris (TPI), and anteriorly located ciliary body (ALCB). The integrated assessment model classifies AC mechanisms into pure PB, pure non-PB, multiple mechanisms (MM), and others. ACM-Assessor's evaluation encompassed external testing (2266 images), human-machine competition and assisting beginners' assessment (an independent test set of 436 images). ACM-Assessor achieved accuracies of 0.924 (PB), 0.925 (TPI), 0.947 (ALCB), and 0.839 (integrated assessment). In man-machine comparisons, the system's accuracy was comparable to experts ( > 0.05). With model assistance, beginners' accuracy improved by 0.117 for binary classification and 0.219 for integrated assessment. ACM-Assessor demonstrates expert-level accuracy and enhances beginners' learning in UBM analysis.
原发性闭角型青光眼（PACG）以房角关闭（AC）且进展隐匿、不可逆转为特征，需要精确评估房角关闭机制以进行准确诊断和治疗。本研究开发了一种人工智能系统ACM-Assessor，用于评估超声生物显微镜（UBM）图像中的房角关闭机制。回顾性收集了1160例患者的8482张UBM图像数据集。ACM-Assessor包括像素到物理间距转换模型、前房角边界分割模型和巩膜突定位模型，以及三个二元分类模型，用于评估瞳孔阻滞（PB）、周边虹膜肥厚（TPI）和睫状体前位（ALCB）。综合评估模型将房角关闭机制分为单纯瞳孔阻滞、单纯非瞳孔阻滞、多种机制（MM）和其他类型。ACM-Assessor的评估包括外部测试（2266张图像）、人机竞赛和辅助初学者评估（436张图像的独立测试集）。ACM-Assessor在瞳孔阻滞（0.924）、周边虹膜肥厚（0.925）、睫状体前位（0.947）和综合评估（0.839）方面均达到了较高的准确率。在人机比较中，该系统的准确率与专家相当（>0.05）。在模型辅助下，初学者在二元分类中的准确率提高了0.117，在综合评估中的准确率提高了0.219。ACM-Assessor在UBM分析中展现出专家级的准确率，并促进了初学者的学习。
