@Article{info:doi/10.2196/44248,
author="Jan, Zainab
and El Assadi, Farah
and Abd-alrazaq, Alaa
and Jithesh, Puthen Veettil",
title="Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review",
journal="J Med Internet Res",
year="2023",
month="Mar",
day="31",
volume="25",
pages="e44248",
keywords="artificial Intelligence; pancreatic cancer; diagnosis; diagnostic; prediction; machine learning; deep learning; scoping; review method; predict; cancer; oncology; pancreatic; algorithm",
abstract="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. ",
issn="1438-8871",
doi="10.2196/44248",
url="https://www.jmir.org/2023/1/e44248",
url="https://doi.org/10.2196/44248",
url="http://www.ncbi.nlm.nih.gov/pubmed/37000507"
}