Kailani Zeena, Kim Lauren, Bierbrier Joshua, Balas Michael, Mathew David J
Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
Queen's School of Medicine, Queen's University, Kingston, ON, Canada.
Front Big Data. 2025 Aug 8;8:1605018. doi: 10.3389/fdata.2025.1605018. eCollection 2025.
Glaucoma is a leading cause of irreversible blindness, and its rising global prevalence has led to a significant increase in glaucoma surgeries. However, predicting postoperative outcomes remains challenging due to the complex interplay of patient factors, surgical techniques, and postoperative care. Artificial intelligence (AI) has emerged as a promising tool for enhancing predictive accuracy in clinical decision-making.
This systematic review was conducted to evaluate the current evidence on the use of AI to predict surgical outcomes in glaucoma patients. A comprehensive search of Medline, Embase, Web of Science, and Scopus was performed. Studies were included if they applied AI models to glaucoma surgery outcome prediction.
Six studies met inclusion criteria, collectively analyzing 4,630 surgeries. A variety of algorithms were applied, including random forests, support vector machines, and neural networks. Overall, AI models consistently outperformed traditional statistical approaches, with the best-performing model achieving an accuracy of 87.5%. Key predictors of outcomes included demographic factors (e.g., age), systemic health indicators (e.g., smoking status and body mass index), and ophthalmic parameters (e.g., baseline intraocular pressure, central corneal thickness, mitomycin C use).
While AI models demonstrated superior performance to traditional statistical approaches, the lack of external validation and standardized surgical success definitions limit their clinical applicability. This review highlights both the promise and the current limitations of artificial intelligence in glaucoma surgery outcome prediction, emphasizing the need for prospective, multicenter studies, publicly available datasets, and standardized evaluation metrics to enhance the generalizability and clinical utility of future models.
https://www.crd.york.ac.uk/PROSPERO/view/CRD42024621758, identifier: CRD42024621758.
青光眼是不可逆性失明的主要原因，其在全球范围内的患病率不断上升，导致青光眼手术量显著增加。然而，由于患者因素、手术技术和术后护理之间复杂的相互作用，预测术后结果仍然具有挑战性。人工智能（AI）已成为提高临床决策预测准确性的一种有前景的工具。
进行了这项系统评价，以评估当前关于使用人工智能预测青光眼患者手术结果的证据。对Medline、Embase、科学引文索引和Scopus进行了全面检索。如果研究将人工智能模型应用于青光眼手术结果预测，则纳入研究。
六项研究符合纳入标准，共分析了4630例手术。应用了多种算法，包括随机森林、支持向量机和神经网络。总体而言，人工智能模型始终优于传统统计方法，表现最佳的模型准确率达到87.5%。结果的关键预测因素包括人口统计学因素（如年龄）、全身健康指标（如吸烟状况和体重指数）和眼科参数（如基线眼压、中央角膜厚度、丝裂霉素C的使用）。
虽然人工智能模型表现出优于传统统计方法的性能，但缺乏外部验证和标准化的手术成功定义限制了它们的临床适用性。本综述强调了人工智能在青光眼手术结果预测中的前景和当前局限性，强调需要进行前瞻性、多中心研究、公开可用的数据集以及标准化评估指标，以提高未来模型的通用性和临床实用性。
https://www.crd.york.ac.uk/PROSPERO/view/CRD42024621758，标识符：CRD42024621758。
