Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis
We performed a meta-analysis of published data to investigate the diagnostic value of artificial intelligence for pancreatic cancer. Systematic research was conducted in the following databases: PubMed, Embase, and Web of Science to identify relevant studies up to October 2021. We extracted or calcu...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/2/309 |
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| author | Elena Adriana Dumitrescu Bogdan Silviu Ungureanu Irina M. Cazacu Lucian Mihai Florescu Liliana Streba Vlad M. Croitoru Daniel Sur Adina Croitoru Adina Turcu-Stiolica Cristian Virgil Lungulescu |
| author_facet | Elena Adriana Dumitrescu Bogdan Silviu Ungureanu Irina M. Cazacu Lucian Mihai Florescu Liliana Streba Vlad M. Croitoru Daniel Sur Adina Croitoru Adina Turcu-Stiolica Cristian Virgil Lungulescu |
| author_sort | Elena Adriana Dumitrescu |
| collection | DOAJ |
| description | We performed a meta-analysis of published data to investigate the diagnostic value of artificial intelligence for pancreatic cancer. Systematic research was conducted in the following databases: PubMed, Embase, and Web of Science to identify relevant studies up to October 2021. We extracted or calculated the number of true positives, false positives true negatives, and false negatives from the selected publications. In total, 10 studies, featuring 1871 patients, met our inclusion criteria. The risk of bias in the included studies was assessed using the QUADAS-2 tool. R and RevMan 5.4.1 software were used for calculations and statistical analysis. The studies included in the meta-analysis did not show an overall heterogeneity (I<sup>2</sup> = 0%), and no significant differences were found from the subgroup analysis. The pooled diagnostic sensitivity and specificity were 0.92 (95% CI, 0.89–0.95) and 0.9 (95% CI, 0.83–0.94), respectively. The area under the summary receiver operating characteristics curve was 0.95, and the diagnostic odds ratio was 128.9 (95% CI, 71.2–233.8), indicating very good diagnostic accuracy for the detection of pancreatic cancer. Based on these promising preliminary results and further testing on a larger dataset, artificial intelligence-assisted endoscopic ultrasound could become an important tool for the computer-aided diagnosis of pancreatic cancer. |
| first_indexed | 2024-03-09T22:12:11Z |
| format | Article |
| id | doaj.art-49f5ca3945014caaa1f5c7b58fc1c81d |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T22:12:11Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-49f5ca3945014caaa1f5c7b58fc1c81d2023-11-23T19:29:53ZengMDPI AGDiagnostics2075-44182022-01-0112230910.3390/diagnostics12020309Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-AnalysisElena Adriana Dumitrescu0Bogdan Silviu Ungureanu1Irina M. Cazacu2Lucian Mihai Florescu3Liliana Streba4Vlad M. Croitoru5Daniel Sur6Adina Croitoru7Adina Turcu-Stiolica8Cristian Virgil Lungulescu9Institute of Oncology, Prof. Dr. Alexandru Trestioreanu, Șoseaua Fundeni, 022328 Bucharest, RomaniaDepartment of Gastroenterology, University of Medicine and Pharmacy Craiova, 2 Petru Rares Str, 200349 Craiova, RomaniaDepartment of Oncology, Fundeni Clinical Institute, 258 Fundeni St, 022238 Bucharest, RomaniaDepartment of Radiology & Medical Imaging, University of Medicine and Pharmacy Craiova, 2-4 Petru Rares St, 200349 Craiova, RomaniaDepartment of Oncology, University of Medicine and Pharmacy Craiova, 2 Petru Rares Str, 200349 Craiova, RomaniaDepartment of Oncology, Fundeni Clinical Institute, 258 Fundeni St, 022238 Bucharest, Romania11th Department of Medical Oncology, University of Medicine and Pharmacy Iuliu Hatieganu, 400012 Cluj-Napoca, RomaniaDepartment of Oncology, Fundeni Clinical Institute, 258 Fundeni St, 022238 Bucharest, RomaniaDepartment of Pharmacoeconomics, University of Medicine and Pharmacy of Craiova, 2 Petru Rares Str, 200349 Craiova, RomaniaDepartment of Oncology, University of Medicine and Pharmacy Craiova, 2 Petru Rares Str, 200349 Craiova, RomaniaWe performed a meta-analysis of published data to investigate the diagnostic value of artificial intelligence for pancreatic cancer. Systematic research was conducted in the following databases: PubMed, Embase, and Web of Science to identify relevant studies up to October 2021. We extracted or calculated the number of true positives, false positives true negatives, and false negatives from the selected publications. In total, 10 studies, featuring 1871 patients, met our inclusion criteria. The risk of bias in the included studies was assessed using the QUADAS-2 tool. R and RevMan 5.4.1 software were used for calculations and statistical analysis. The studies included in the meta-analysis did not show an overall heterogeneity (I<sup>2</sup> = 0%), and no significant differences were found from the subgroup analysis. The pooled diagnostic sensitivity and specificity were 0.92 (95% CI, 0.89–0.95) and 0.9 (95% CI, 0.83–0.94), respectively. The area under the summary receiver operating characteristics curve was 0.95, and the diagnostic odds ratio was 128.9 (95% CI, 71.2–233.8), indicating very good diagnostic accuracy for the detection of pancreatic cancer. Based on these promising preliminary results and further testing on a larger dataset, artificial intelligence-assisted endoscopic ultrasound could become an important tool for the computer-aided diagnosis of pancreatic cancer.https://www.mdpi.com/2075-4418/12/2/309artificial intelligencedeep learningcomputer-aided diagnosispancreatic cancerendoscopic ultrasound |
| spellingShingle | Elena Adriana Dumitrescu Bogdan Silviu Ungureanu Irina M. Cazacu Lucian Mihai Florescu Liliana Streba Vlad M. Croitoru Daniel Sur Adina Croitoru Adina Turcu-Stiolica Cristian Virgil Lungulescu Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis Diagnostics artificial intelligence deep learning computer-aided diagnosis pancreatic cancer endoscopic ultrasound |
| title | Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis |
| title_full | Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis |
| title_fullStr | Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis |
| title_full_unstemmed | Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis |
| title_short | Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis |
| title_sort | diagnostic value of artificial intelligence assisted endoscopic ultrasound for pancreatic cancer a systematic review and meta analysis |
| topic | artificial intelligence deep learning computer-aided diagnosis pancreatic cancer endoscopic ultrasound |
| url | https://www.mdpi.com/2075-4418/12/2/309 |
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