- 期刊
人工智慧輔助子宮輸卵管攝影暢通性自動分類
Artificial Intelligence-assisted Automatic Classification of Patency in Hysterosalpingography
摘要
不孕症是指在規律性行為下一年未能懷孕的狀態，影響著全球數千萬人口。子宮輸卵管攝影（hysterosalpingography, HSG）作為一個常規的影像檢查工具，能有效評估輸卵管的暢通性。本研究旨在探討人工智慧（artificial intelligence, AI）使用卷積神經網絡（convolutional neural network, CNN）和遷移式學習技術自動判讀影像的潛力。回溯納入於本院接受HSG檢查的不孕症患者的影像資料，經由兩位放射診斷科專科醫師判讀和標註。透過遷移式學習、資料擴增技術、以及多種超參數的設定來優化模型的性能，進行雙側暢通或任一側阻塞的雙分類預測。研究結果顯示，使用730例有限訓練資料，以shufflenet v2所訓練的模型，在91例測試資料集達到90%的準確率，召回率為93%，F1數值為93%，精確度為94%，特異性為82%，陰性預測值為78%。透過熱區圖的分析，模型展現了正確識別和關注輸卵管在影像中的位置和形態的能力。總結，CNN模型在自動分類HSG影像的輸卵管暢通與否，具有可行性和有效性，通過進一步的研究和優化，我們期待模型未來能實際應用於臨床，實踐人工智慧輔助醫療。
並列摘要
Infertility, the failure to conceive within a year despite regular sexual intercourse, affects tens of millions of people around the world. Hysterosalpingography (HSG) is a routine imaging tool, can effectively evaluate the patency of the fallopian tubes. This study aims to explore the potential of artificial intelligence (AI) to automatically interpret images using convolutional neural network (CNN) and transfer learning technology. The imaging data of infertility patients who underwent HSG examination in our hospital were retrospectively included, and were interpreted and annotated by two radiologists. The performance of the model is optimized through transfer learning, data amplification technology, and various hyperparameter settings to perform binary classification predictions of both patency or any one side obstruction. The research results show that using 730 limited training data, the model trained with shufflenet v2 achieved an accuracy of 90%, a recall rate of 93%, an F1 value of 93%, and a precision of 94% in the 91 test dataset. The specificity was 82% and the negative predictive value was 78%. Through the analysis of heat map, the model demonstrated the ability to correctly identify and focus on the position and shape of the fallopian tube in the image. In summary, the CNN model is feasible and effective in automatically classifying whether the fallopian tube is patent in HSG images. Through further research and optimization, we hope that the model can be actually used in clinical practice in the future to practice artificial intelligence-assisted medical treatment.