Abstract：
In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI\'s transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI\'s role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI\'s progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare\'s carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI\'s clinical adoption, marking an era where AI not only assists but augments ophthalmic care.
摘要：
在过去的十年里，人工智能(AI)对眼科产生了重大影响，特别是在管理角膜疾病方面，失明的主要可逆原因。这篇综述探讨了人工智能在角膜亚专业中的转化作用，它采用了先进的技术来获得卓越的临床判断，早期诊断,个性化治疗。虽然与青光眼和视网膜病变相比，AI在眼前段疾病中的作用记录较少，这篇综述强调了它通过像裂隙灯生物显微镜这样的成像技术整合到角膜诊断中，眼前节光学相干断层扫描（AS-OCT），和体内共聚焦生物显微镜。人工智能在完善圆锥角膜等疾病的决策和预后方面发挥了关键作用，感染性角膜炎，和营养不良。多疾病深度学习神经网络（MDDN）已显示出使用AS-OCT图像对角膜疾病进行分类的诊断能力。实现显著的指标，如AUC为0.910。人工智能在过去20年中的进展显著提高了诊断圆锥角膜和微生物性角膜炎等疾病的准确性。例如,AI在对细菌性和真菌性角膜炎进行分类方面的准确率为90.7%，在区分各种角膜疾病方面的AUC为0.910。卷积神经网络（CNN）增强了对彩色编码角膜图的分析，对圆锥角膜的诊断准确率高达99.3%。深度学习算法在体内共聚焦显微镜上检测真菌菌丝方面也显示出强大的性能，与菌丝密度的精确量化。根据Amsler-Krumeich分类，结合断层扫描和视力的AI模型在圆锥角膜分期中显示出高达97％的准确性。然而,审查承认当前人工智能模型的局限性，包括他们对二元分类的依赖，这可能无法捕捉到多种并存疾病的真实世界临床表现的复杂性。挑战还包括对数据质量的依赖，不同的成像协议，并集成多模态图像以进行广义AI诊断。强调AI模型对可解释性的需求，以促进临床环境中的信任和适用性。展望未来,人工智能有可能解开角膜病理背后的复杂机制，减少医疗保健的碳足迹，并彻底改变诊断和管理范式。道德和监管考虑将伴随AI的临床采用，标志着人工智能不仅有助于而且增强眼科护理的时代。
