人工智慧與白內障手術

Artificial Intelligence and Cataract Surgery

白內障是全球失明的主要原因，唯一有效的治療方法是手術，需移除混濁的白內障，並植入人工水晶體。由於眼部結構精細，需要極高的精準度與判斷力，才能順利完成手術，但過去只能仰賴醫師養成過程的經驗，來克服病人個體差異及手術中無法預測的各種突發情況。為提升手術安全性與效率，人工智慧技術成為重要助力。AI可應用於術前診斷、術中導航與術後追蹤，例如可使用深度學習模型分析手術影片，提供即時反饋與風險檢測。核心技術包括卷積神經網路（convolutional neural network, CNN）提取影像特徵丶長短期記憶網路（long short-term memory, LSTM）分析手術階段，以及U-Net分割眼部結構，有助於識別手術工具、白內障類型與關鍵步驟，提高手術準確性。建立白內障手術資料庫是AI發展關鍵，提供標註數據可達成階段識別與異常檢測，幫助優化流程並預測術後風險。然而，數據收集面臨隱私與標註難度挑戰。未來，AI將實現個人化手術方案，如選擇最佳人工水晶體，並提升培訓效率，減少醫療負擔，最終改善病人視力與生活品質。

Cataracts are the leading cause of blindness worldwide, and surgery is the only effective treatment, requiring the removal of the cloudy lens and implantation of an artificial intraocular lens (IOL). Due to the intricate structure of the eye, achieving successful surgery demands exceptional precision and decision-making skills. Traditionally, surgeons have relied solely on their training and experience to navigate patient-specific variations and unpredictable intraoperative events. To enhance surgical safety and efficiency, artificial intelligence (AI) has become a crucial tool. AI can be applied to preoperative diagnosis, intraoperative navigation, and postoperative monitoring. For example, deep learning models can analyze surgical videos to provide real-time feedback and risk detection. Key AI technologies include convolutional neural networks (CNNs) for image feature extraction, long short-term memory (LSTM) networks for surgical phase recognition, and U-Net for segmenting ocular structures. These techniques assist in identifying surgical instruments, cataract types, and critical procedural steps, improving surgical accuracy. Building a comprehensive cataract surgery database is essential for AI development. Annotated datasets enable surgical phase recognition and anomaly detection, optimizing procedures and predicting postoperative risks. However, data collection faces challenges such as privacy concerns and annotation complexity. In the future, AI will enable personalized surgical strategies, such as selecting the optimal IOL, enhancing training efficiency, and reducing the healthcare burden. Ultimately, AI-driven advancements in cataract surgery will improve patients' vision and quality of life.