Artificial intelligence for cervical cancer screening : Scoping review, 2009-2022
© 2023 The Authors. International Journal of Gynecology & Obstetrics published by John Wiley & Sons Ltd on behalf of International Federation of Gynecology and Obstetrics..
BACKGROUND: The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images.
OBJECTIVES: To describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR).
SEARCH STRATEGY: Arksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords.
SELECTION CRITERIA: Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases.
DATA COLLECTION AND ANALYSIS: A descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance.
MAIN RESULTS: We identified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. The machine learning/deep learning (DL) algorithms applied in the articles included support vector machine (SVM), random forest classifier, k-nearest neighbors, multilayer perceptron, C4.5, Naïve Bayes, AdaBoost, XGboots, conditional random fields, Bayes classifier, convolutional neural network (CNN; and variations), ResNet (several versions), YOLO+EfficientNetB0, and visual geometry group (VGG; several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97%.
CONCLUSION: We concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising. However, further research is necessary to validate these findings.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:165 |
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| Enthalten in: |
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics - 165(2024), 2 vom: 12. Apr., Seite 566-578 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Vargas-Cardona, Hernán Darío [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 17.04.2024 Date Revised 17.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/ijgo.15179 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM363047808 |
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| 100 | 1 | |a Vargas-Cardona, Hernán Darío |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Artificial intelligence for cervical cancer screening |b Scoping review, 2009-2022 |
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| 500 | |a Date Revised 17.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 The Authors. International Journal of Gynecology & Obstetrics published by John Wiley & Sons Ltd on behalf of International Federation of Gynecology and Obstetrics. | ||
| 520 | |a BACKGROUND: The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images | ||
| 520 | |a OBJECTIVES: To describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) | ||
| 520 | |a SEARCH STRATEGY: Arksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords | ||
| 520 | |a SELECTION CRITERIA: Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases | ||
| 520 | |a DATA COLLECTION AND ANALYSIS: A descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance | ||
| 520 | |a MAIN RESULTS: We identified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. The machine learning/deep learning (DL) algorithms applied in the articles included support vector machine (SVM), random forest classifier, k-nearest neighbors, multilayer perceptron, C4.5, Naïve Bayes, AdaBoost, XGboots, conditional random fields, Bayes classifier, convolutional neural network (CNN; and variations), ResNet (several versions), YOLO+EfficientNetB0, and visual geometry group (VGG; several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97% | ||
| 520 | |a CONCLUSION: We concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising. However, further research is necessary to validate these findings | ||
| 650 | 4 | |a Systematic Review | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a cervical cancer | |
| 650 | 4 | |a clinical diagnosis | |
| 650 | 4 | |a colposcopy | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a mass screening | |
| 650 | 4 | |a uterine cervical neoplasms | |
| 700 | 1 | |a Rodriguez-Lopez, Mérida |e verfasserin |4 aut | |
| 700 | 1 | |a Arrivillaga, Marcela |e verfasserin |4 aut | |
| 700 | 1 | |a Vergara-Sanchez, Carlos |e verfasserin |4 aut | |
| 700 | 1 | |a García-Cifuentes, Juan P |e verfasserin |4 aut | |
| 700 | 1 | |a Bermúdez, Paula C |e verfasserin |4 aut | |
| 700 | 1 | |a Jaramillo-Botero, Andres |e verfasserin |4 aut | |
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