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Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review.

Authors :
Jan Z
El Assadi F
Abd-Alrazaq A
Jithesh PV
Source :
Journal of medical Internet research [J Med Internet Res] 2023 Mar 31; Vol. 25, pp. e44248. Date of Electronic Publication: 2023 Mar 31.
Publication Year :
2023

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.<br />Objective: This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.<br />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.<br />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.<br />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.<br /> (©Zainab Jan, Farah El Assadi, Alaa Abd-alrazaq, Puthen Veettil Jithesh. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.03.2023.)

Details

Language :
English
ISSN :
1438-8871
Volume :
25
Database :
MEDLINE
Journal :
Journal of medical Internet research
Publication Type :
Academic Journal
Accession number :
37000507
Full Text :
https://doi.org/10.2196/44248