%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 25
%N 
%P e44248
%T Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review
%A Jan,Zainab
%A El Assadi,Farah
%A Abd-alrazaq,Alaa
%A Jithesh,Puthen Veettil
%+ College of Health & Life Sciences, Hamad Bin Khalifa University, Penrose House, Education City, Doha, 34110, Qatar, 974 44547438, jveettil@hbku.edu.qa
%K artificial Intelligence
%K pancreatic cancer
%K diagnosis
%K diagnostic
%K prediction
%K machine learning
%K deep learning
%K scoping
%K review method
%K predict
%K cancer
%K oncology
%K pancreatic
%K algorithm
%D 2023
%7 31.3.2023
%9 Review
%J J Med Internet Res
%G English
%X Background: Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer. Objective: This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature. Methods: A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results: Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. Conclusions: This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care. 
%M 37000507
%R 10.2196/44248
%U https://www.jmir.org/2023/1/e44248
%U https://doi.org/10.2196/44248
%U http://www.ncbi.nlm.nih.gov/pubmed/37000507