AI助力青光眼早期诊断，提升识别准确率与可解释性
AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review
- 综述深度学习在青光眼辅助诊断中的应用方法
- 指出当前系统在安全性与可解释性方面的不足
- 适合眼科医生与医疗AI研究者参考
青光眼是一组以视神经损伤为特征、可能导致失明的眼病，常被称为“无声的视力窃贼”，早期无明显症状。因此，早期检测对防止视力丧失至关重要。随着人工智能（AI）特别是深度学习（DL）技术的发展，计算机辅助诊断（CADx）系统已成为帮助临床医生早期精准诊断青光眼的有前景工具。本文旨在全面回顾用于青光眼诊断的AI技术在CADx系统中的应用。通过对现有文献的详细分析，识别出当前系统在安全性、可靠性、可解释性与可追溯性方面的关键缺陷与挑战，并强调改进这些方面的重要性。通过揭示研究空白，本文致力于推动青光眼早期诊断中CADx系统的发展，以最大限度减少潜在的视力损失。
原文摘要 · Abstract (English)
The diagnosis of glaucoma plays a critical role in the management and treatment of this vision-threatening disease. Glaucoma is a group of eye diseases that cause blindness by damaging the optic nerve at the back of the eye. Often called "silent thief of sight", it exhibits no symptoms during the early stages. Therefore, early detection is crucial to prevent vision loss. With the rise of Artificial Intelligence (AI), particularly Deep Learning (DL) techniques, Computer-Aided Diagnosis (CADx) systems have emerged as promising tools to assist clinicians in accurately diagnosing glaucoma early. This paper aims to provide a comprehensive overview of AI techniques utilized in CADx systems for glaucoma diagnosis. Through a detailed analysis of current literature, we identify key gaps and challenges in these systems, emphasizing the need for improved safety, reliability, interpretability, and explainability. By identifying research gaps, we aim to advance the field of CADx systems especially for the early diagnosis of glaucoma, in order to prevent any potential loss of vision.
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