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Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131763/ https://www.ncbi.nlm.nih.gov/pubmed/37000507 http://dx.doi.org/10.2196/44248 |
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author | Jan, Zainab El Assadi, Farah Abd-alrazaq, Alaa Jithesh, Puthen Veettil |
author_facet | Jan, Zainab El Assadi, Farah Abd-alrazaq, Alaa Jithesh, Puthen Veettil |
author_sort | Jan, Zainab |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10131763 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-101317632023-04-27 Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review Jan, Zainab El Assadi, Farah Abd-alrazaq, Alaa Jithesh, Puthen Veettil J Med Internet Res Review 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. JMIR Publications 2023-03-31 /pmc/articles/PMC10131763/ /pubmed/37000507 http://dx.doi.org/10.2196/44248 Text en ©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. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Review Jan, Zainab El Assadi, Farah Abd-alrazaq, Alaa Jithesh, Puthen Veettil Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review |
title | Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review |
title_full | Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review |
title_fullStr | Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review |
title_full_unstemmed | Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review |
title_short | Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review |
title_sort | artificial intelligence for the prediction and early diagnosis of pancreatic cancer: scoping review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131763/ https://www.ncbi.nlm.nih.gov/pubmed/37000507 http://dx.doi.org/10.2196/44248 |
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