Multimodal convolutional neural network-based algorithm for real-time detection and differentiation of malignant and inflammatory biliary strictures in cholangioscopy: a proof-of-concept study (with video).
Journal:
Gastrointestinal endoscopy
PMID:
39265745
Abstract
BACKGROUND AND AIMS: Deep learning algorithms gained attention for detection (computer-aided detection [CADe]) of biliary tract cancer in digital single-operator cholangioscopy (dSOC). We developed a multimodal convolutional neural network (CNN) for detection (CADe), characterization and discriminating (computer-aided diagnosis [CADx]) between malignant, inflammatory, and normal biliary tissue in raw dSOC videos. In addition, clinical metadata were included in the CNN algorithm to overcome limitations of image-only models.
Authors
Keywords
Aged
Algorithms
Bile Duct Neoplasms
Biliary Tract Neoplasms
Cholangiocarcinoma
Cholangitis
Cholestasis
Constriction, Pathologic
Convolutional Neural Networks
Deep Learning
Diagnosis, Computer-Assisted
Diagnosis, Differential
Endoscopy, Digestive System
Female
Humans
Male
Middle Aged
Neural Networks, Computer
Proof of Concept Study
ROC Curve
Sensitivity and Specificity
Video Recording