Abstract: Preoperative diagnosis of bile duct tumor thrombus (BDTT) is clinically important as the surgical prognosis of hepatocellular carcinoma (HCC) patients with BDTT is significantly different from that of patients without BDTT. Although dilated bile ducts (DBDs) can act as biomarkers for diagnosing BDTT, it is easy for doctors to ignore DBDs when reporting the imaging scan result, leading to a high missed diagnosis rate in practice. This study aims to develop an artificial intelligence (AI) pipeline for automatically diagnosing HCC patients with BDTT using medical images. The proposed AI pipeline includes two stages. First, the object detection neural network Faster R-CNN was adopted to identify DBDs; then, an HCC patient was diagnosed with BDTT if the proportion of images with at least one identified DBD exceeded some threshold value. Based on 2354 CT images collected from 32 HCC patients (16 with BDTT and 16 without BDTT, 1∶1 matched), the proposed AI pipeline achieves an average true positive rate of 0.92 for identifying DBDs per patient and a patient-level true positive rate of 0.81 for diagnosing BDTT. The AUC value of the patient-level diagnosis of BDTT is 0.94 (95% CI: 0.87, 1.00), compared with 0.71 (95% CI: 0.51, 0.90) achieved by random forest based on preoperative clinical variables. The high accuracies demonstrate that the proposed AI pipeline is successful in the diagnosis and localization of BDTT using CT images.