Tonti Emanuele, Tonti Sofia, Mancini Flavia, Bonini Chiara, Spadea Leopoldo, D'Esposito Fabiana, Gagliano Caterina, Musa Mutali, Zeppieri Marco
UOC Ophthalmology, Sant'Eugenio Hospital, 00144 Rome, Italy.
Biomedical Engineering, Politecnico di Torino, 10129 Turin, Italy.
J Pers Med. 2024 Oct 16;14(10):1062. doi: 10.3390/jpm14101062.
Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions.
This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions.
A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included.
The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care.
Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.
青光眼是全球不可逆性失明的主要原因，需要根据个体患者特征制定精确的管理策略。人工智能（AI）有望通过提供个性化干预措施，彻底改变青光眼的治疗方法。
本综述探讨了人工智能在青光眼患者个性化管理中的应用现状，重点介绍了进展、挑战和未来方向。
对包括PubMed、Scopus和Web of Science在内的电子数据库进行系统检索，以识别截至2024年发表的相关研究。纳入探索人工智能技术在青光眼患者个性化管理策略中应用的研究。
该综述确定了人工智能在青光眼管理中的多种应用，从早期检测和诊断到治疗优化和预后预测。机器学习算法，特别是深度学习模型，在通过光学相干断层扫描（OCT）和视野测试等各种成像方式诊断青光眼方面显示出高精度。人工智能驱动的风险分层工具通过将患者特定数据与预测分析相结合，促进了个性化治疗决策，提高了治疗效果，同时将不良反应降至最低。此外，基于人工智能的远程眼科平台实现了远程监测和及时干预，改善了患者获得专科护理的机会。
将人工智能技术整合到青光眼患者的个性化管理中，在优化临床决策、提高治疗效果和减缓疾病进展方面具有巨大潜力。然而，数据异质性、模型可解释性和监管问题等挑战值得进一步研究。未来的研究应专注于改进人工智能算法，通过大规模前瞻性研究验证其临床效用，并确保无缝融入常规临床实践，以实现个性化青光眼护理的全部益处。
