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Application of artificial intelligence for diagnosis of pancreatic ductal adenocarcinoma by EUS: A systematic review and meta-analysis

EUS-guided tissue acquisition carries certain risks from unnecessary needle puncture in the low-likelihood lesions. Artificial intelligence (AI) system may enable us to resolve these limitations. We aimed to assess the performance of AI-assisted diagnosis of pancreatic ductal adenocarcinoma (PDAC) b...

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Autores principales: Prasoppokakorn, Thaninee, Tiyarattanachai, Thodsawit, Chaiteerakij, Roongruedee, Decharatanachart, Pakanat, Mekaroonkamol, Parit, Ridtitid, Wiriyaporn, Kongkam, Pradermchai, Rerknimitr, Rungsun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8887033/
https://www.ncbi.nlm.nih.gov/pubmed/34937308
http://dx.doi.org/10.4103/EUS-D-20-00219
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author Prasoppokakorn, Thaninee
Tiyarattanachai, Thodsawit
Chaiteerakij, Roongruedee
Decharatanachart, Pakanat
Mekaroonkamol, Parit
Ridtitid, Wiriyaporn
Kongkam, Pradermchai
Rerknimitr, Rungsun
author_facet Prasoppokakorn, Thaninee
Tiyarattanachai, Thodsawit
Chaiteerakij, Roongruedee
Decharatanachart, Pakanat
Mekaroonkamol, Parit
Ridtitid, Wiriyaporn
Kongkam, Pradermchai
Rerknimitr, Rungsun
author_sort Prasoppokakorn, Thaninee
collection PubMed
description EUS-guided tissue acquisition carries certain risks from unnecessary needle puncture in the low-likelihood lesions. Artificial intelligence (AI) system may enable us to resolve these limitations. We aimed to assess the performance of AI-assisted diagnosis of pancreatic ductal adenocarcinoma (PDAC) by off-line evaluating the EUS images from different modes. The databases PubMed, EMBASE, SCOPUS, ISI, IEEE, and Association for Computing Machinery were systematically searched for relevant studies. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic curve were estimated using R software. Of 369 publications, 8 studies with a total of 870 PDAC patients were included. The pooled sensitivity and specificity of AI-assisted EUS were 0.91 (95% confidence interval [CI], 0.87–0.93) and 0.90 (95% CI, 0.79–0.96), respectively, with DOR of 81.6 (95% CI, 32.2–207.3), for diagnosis of PDAC. The area under the curve was 0.923. AI-assisted B-mode EUS had pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.91, 0.90, 0.94, and 0.84, respectively; while AI-assisted contrast-enhanced EUS and AI-assisted EUS elastography had sensitivity, specificity, PPV, and NPV of 0.95, 0.95, 0.97, and 0.90; and 0.88, 0.83, 0.96 and 0.57, respectively. AI-assisted EUS has a high accuracy rate and may potentially enhance the performance of EUS by aiding the endosonographers to distinguish PDAC from other solid lesions. Validation of these findings in other independent cohorts and improvement of AI function as a real-time diagnosis to guide for tissue acquisition are warranted.
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spelling pubmed-88870332022-03-10 Application of artificial intelligence for diagnosis of pancreatic ductal adenocarcinoma by EUS: A systematic review and meta-analysis Prasoppokakorn, Thaninee Tiyarattanachai, Thodsawit Chaiteerakij, Roongruedee Decharatanachart, Pakanat Mekaroonkamol, Parit Ridtitid, Wiriyaporn Kongkam, Pradermchai Rerknimitr, Rungsun Endosc Ultrasound Review Article EUS-guided tissue acquisition carries certain risks from unnecessary needle puncture in the low-likelihood lesions. Artificial intelligence (AI) system may enable us to resolve these limitations. We aimed to assess the performance of AI-assisted diagnosis of pancreatic ductal adenocarcinoma (PDAC) by off-line evaluating the EUS images from different modes. The databases PubMed, EMBASE, SCOPUS, ISI, IEEE, and Association for Computing Machinery were systematically searched for relevant studies. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic curve were estimated using R software. Of 369 publications, 8 studies with a total of 870 PDAC patients were included. The pooled sensitivity and specificity of AI-assisted EUS were 0.91 (95% confidence interval [CI], 0.87–0.93) and 0.90 (95% CI, 0.79–0.96), respectively, with DOR of 81.6 (95% CI, 32.2–207.3), for diagnosis of PDAC. The area under the curve was 0.923. AI-assisted B-mode EUS had pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.91, 0.90, 0.94, and 0.84, respectively; while AI-assisted contrast-enhanced EUS and AI-assisted EUS elastography had sensitivity, specificity, PPV, and NPV of 0.95, 0.95, 0.97, and 0.90; and 0.88, 0.83, 0.96 and 0.57, respectively. AI-assisted EUS has a high accuracy rate and may potentially enhance the performance of EUS by aiding the endosonographers to distinguish PDAC from other solid lesions. Validation of these findings in other independent cohorts and improvement of AI function as a real-time diagnosis to guide for tissue acquisition are warranted. Wolters Kluwer - Medknow 2021-12-15 /pmc/articles/PMC8887033/ /pubmed/34937308 http://dx.doi.org/10.4103/EUS-D-20-00219 Text en Copyright: © 2021 SPRING MEDIA PUBLISHING CO. LTD https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Review Article
Prasoppokakorn, Thaninee
Tiyarattanachai, Thodsawit
Chaiteerakij, Roongruedee
Decharatanachart, Pakanat
Mekaroonkamol, Parit
Ridtitid, Wiriyaporn
Kongkam, Pradermchai
Rerknimitr, Rungsun
Application of artificial intelligence for diagnosis of pancreatic ductal adenocarcinoma by EUS: A systematic review and meta-analysis
title Application of artificial intelligence for diagnosis of pancreatic ductal adenocarcinoma by EUS: A systematic review and meta-analysis
title_full Application of artificial intelligence for diagnosis of pancreatic ductal adenocarcinoma by EUS: A systematic review and meta-analysis
title_fullStr Application of artificial intelligence for diagnosis of pancreatic ductal adenocarcinoma by EUS: A systematic review and meta-analysis
title_full_unstemmed Application of artificial intelligence for diagnosis of pancreatic ductal adenocarcinoma by EUS: A systematic review and meta-analysis
title_short Application of artificial intelligence for diagnosis of pancreatic ductal adenocarcinoma by EUS: A systematic review and meta-analysis
title_sort application of artificial intelligence for diagnosis of pancreatic ductal adenocarcinoma by eus: a systematic review and meta-analysis
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8887033/
https://www.ncbi.nlm.nih.gov/pubmed/34937308
http://dx.doi.org/10.4103/EUS-D-20-00219
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