Training and validation of deep learning for the detection of malignant bile duct stenosis in fluoroscopy images of endoscopic retrograde cholangiopancreatography

Aims Accurate distinction between benign and malignant biliary strictures (BS) is challenging. The use of bile duct biopsies and brush cytology via endoscopic retrograde cholangiopancreaticography (ECRP) remains suboptimal. Single-operator cholangioscopy increases the diagnostic yield in BS but has limited availability and high costs. Convolutional neural network (CNN)-based systems have the potential to assist in the diagnostic process and improve reproducibility. Thus, we assessed the feasibility of using deep learning to differentiate BS out of fluoroscopy images during ERCP.

Methods We conducted a retrospective review of adult patients (n=251) from three university centers in Germany (Leipzig, Dresden, Halle) who underwent an ERCP. We developed and evaluated a deep learning-based model (DenseNet) by means of fluoroscopy images. We measured the area under the receiver operating characteristic curve (AUROC) to evaluate the performance of the classifier and used saliency maps analyses to understand the decision-making process of the model.

Results In cross-validation (Leipzig cohort), malignant BS were detected with an mean AUROC of 0.88±0.02. On two independent external validation cohorts (Dresden, Halle), the of the deep learned based classifier reached a mean AUROC of of 0.71+±+0.04 and 0.74+±+0.07, respectively. The artificial intelligence model's predictions identify plausible characteristics within the fluoroscopy images.

Conclusions By using a deep learning model, we were able to discriminate malignant BS from benign biliary conditions. Artificial intelligence improves the diagnostic yield of malignant BS and needs to be validated in prospective design.

Publikationsverlauf

Artikel online veröffentlicht:
15. April 2024