TY  - JOUR
AU  - Jan, Zainab
AU  - El Assadi, Farah
AU  - Abd-alrazaq, Alaa
AU  - Jithesh, Puthen Veettil
PY  - 2023
DA  - 2023/3/31
TI  - Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review
JO  - J Med Internet Res
SP  - e44248
VL  - 25
KW  - artificial Intelligence
KW  - pancreatic cancer
KW  - diagnosis
KW  - diagnostic
KW  - prediction
KW  - machine learning
KW  - deep learning
KW  - scoping
KW  - review method
KW  - predict
KW  - cancer
KW  - oncology
KW  - pancreatic
KW  - algorithm
AB  - 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e44248
UR  - https://doi.org/10.2196/44248
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37000507
DO  - 10.2196/44248
ID  - info:doi/10.2196/44248
ER  -