人工智慧與視網膜影像

Artificial Intelligence in Retinal Imaging

自從人工智慧（AI）在2017年擊敗圍棋世界冠軍後，其技術能力以驚人的速度進步，尤其是在醫學影像領域。糖尿病視網膜病變（DR）是第一個訓練AI進行診斷的眼科疾病，其診斷準璀率已超過90%。如今經美國FDA認證的AI工具已應用於臨床協助診斷DR。除了疾病診斷，AI還能透過視網膜影像預測疾病風險、治療成效，甚至檢測全身性疾病。隨著彩色眼底攝影和光學同調斷層掃描（OCT）等技術的進步，視網膜影像的診斷大幅進步，而AI的加入進一步實現了影像分析的自動化，能準確識別疾病特徵並輔助臨床決策。AI可藉由分析視網膜特徵來評估心血管疾病，或阿茲海默症等慢性疾病的風險，展現了視網膜作為全身健康指標的潛力。雖然AI在應用上仍面臨數據偏差與模型解釋性不足等挑戰，但它透過遠距醫療顯著提升了診斷效率與準確性，這在資源有限的地區尤為重要。AI作為醫師約得力助手，正加速推動眼科診療的革新，為病人提供更精準且高效的醫療服務。

Since AI triumphed over the world champion in Go in 2017, its capabilities have advanced at an astonishing pace, especially in medical imaging. Diabetic retinopathy (DR) was the first eye disease to be used for training AI in diagnostics, achieving over 90% accuracy. Today, FDA-approved AI tools are actively used in clinical practice to assist in DR diagnosis. Beyond disease detection, AI can predict the onset of illnesses, treatment outcomes, and even systemic conditions by analyzing retinal images. Advances in imaging techniques like fundus photography and optical coherence tomography (OCT) have significantly improved retinal diagnostics, and AI has enhanced their potential by automating image analysis, identifying disease markers, and aiding clinical decisions. AI can assess cardiovascular risks and chronic diseases like Alzheimer’s by detecting subtle retinal features, showcasing the retina’s role as a biomarker for systemic health. Despite challenges like data bias and interpretability, AI has improved diagnostic accuracy and efficiency, particularly in underserved regions through telemedicine. By complementing medical expertise, AI is transforming ophthalmology, offering more precise and efficient care for patients.