Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review

• Published in: International journal of medical informatics (Shannon, Ireland) Vol. 174; p. 105044
• Main Authors: Yin, Minyue, Liu, Lu, Gao, Jingwen, Lin, Jiaxi, Qu, Shuting, Xu, Wei, Liu, Xiaolin, Xu, Chunfang, Zhu, Jinzhou
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.06.2023
• Subjects: Algorithms; Convolutional neural networks; Deep Learning; Endoscopic ultrasonography; Endosonography - methods; Humans; Neural Networks, Computer; Pancreatic diseases; Pancreatic Diseases - diagnostic imaging; Systematic review; Deep learning; Systematic review; Pancreatic diseases; Convolutional neural networks; Endoscopic ultrasonography
• ISSN: 1386-5056; 1872-8243; 1872-8243
• DOI: 10.1016/j.ijmedinf.2023.105044

•This study aims to review the application of deep learning in EUS-based diagnosis of pancreatic diseases.•We find a shift of research emphasis from single-modal to multi-modal (image-, video- and voice-based) model development.•This study finds a shift of model architecture from simple to complex for the improved performance.•This study displays the problems mainly include data characteristics and data preprocessing based on the IJMEDI checklist.•This study suggests that data-and codes-sharing benefit peer reproducibility and clinical practices. Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases. Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist. A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points. DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.