Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review
Background. Melanomas, the most common human malignancy, are primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy, and histopathological examination. We aimed to systematically review the performance and quality of mach...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Mediators of Inflammation |
| Online Access: | http://dx.doi.org/10.1155/2022/1734327 |
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| _version_ | 1849435426851389440 |
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| author | Suli Li Yihang Chu Ying Wang Yantong Wang Shipeng Hu Xiangye Wu Xinwei Qi |
| author_facet | Suli Li Yihang Chu Ying Wang Yantong Wang Shipeng Hu Xiangye Wu Xinwei Qi |
| author_sort | Suli Li |
| collection | DOAJ |
| description | Background. Melanomas, the most common human malignancy, are primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy, and histopathological examination. We aimed to systematically review the performance and quality of machine learning-based methods in distinguishing melanoma and benign nevus in the relevant literature. Method. Four databases (Web of Science, PubMed, Embase, and the Cochrane library) were searched to retrieve the relevant studies published until March 26, 2022. The Predictive model Deviation Risk Assessment tool (PROBAST) was used to assess the deviation risk of opposing law. Result. This systematic review included thirty researches with 114007 subjects and 71 machine learning models. The convolutional neural network was the main machine learning method. The pooled sensitivity was 85% (95% CI 82–87%), the specificity was 86% (82–88%), and the C-index was 0.87 (0.84–0.90). Conclusion. The findings of our study showed that ML algorithms had high sensitivity and specificity for distinguishing between melanoma and benign nevi. This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. However, a large proportion of the earlier published studies had methodological flaws, such as lack of external validation and lack of clinician comparisons. The results of these studies should be interpreted with caution. |
| format | Article |
| id | doaj-art-e56eb03159f84f5fb3a3886a6ae5b53f |
| institution | Kabale University |
| issn | 1466-1861 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Mediators of Inflammation |
| spelling | doaj-art-e56eb03159f84f5fb3a3886a6ae5b53f2025-08-20T03:26:17ZengWileyMediators of Inflammation1466-18612022-01-01202210.1155/2022/1734327Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic ReviewSuli Li0Yihang Chu1Ying Wang2Yantong Wang3Shipeng Hu4Xiangye Wu5Xinwei Qi6First Affiliated Hospital of Xinjiang Medical UniversityCentral South University of Forestry and TechnologyFirst Affiliated Hospital of Xinjiang Medical UniversityCHD UniversityCentral South University of Forestry and TechnologyCentral South University of Forestry and TechnologyFirst Affiliated Hospital of Xinjiang Medical UniversityBackground. Melanomas, the most common human malignancy, are primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy, and histopathological examination. We aimed to systematically review the performance and quality of machine learning-based methods in distinguishing melanoma and benign nevus in the relevant literature. Method. Four databases (Web of Science, PubMed, Embase, and the Cochrane library) were searched to retrieve the relevant studies published until March 26, 2022. The Predictive model Deviation Risk Assessment tool (PROBAST) was used to assess the deviation risk of opposing law. Result. This systematic review included thirty researches with 114007 subjects and 71 machine learning models. The convolutional neural network was the main machine learning method. The pooled sensitivity was 85% (95% CI 82–87%), the specificity was 86% (82–88%), and the C-index was 0.87 (0.84–0.90). Conclusion. The findings of our study showed that ML algorithms had high sensitivity and specificity for distinguishing between melanoma and benign nevi. This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. However, a large proportion of the earlier published studies had methodological flaws, such as lack of external validation and lack of clinician comparisons. The results of these studies should be interpreted with caution.http://dx.doi.org/10.1155/2022/1734327 |
| spellingShingle | Suli Li Yihang Chu Ying Wang Yantong Wang Shipeng Hu Xiangye Wu Xinwei Qi Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review Mediators of Inflammation |
| title | Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review |
| title_full | Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review |
| title_fullStr | Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review |
| title_full_unstemmed | Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review |
| title_short | Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review |
| title_sort | distinguish the value of the benign nevus and melanomas using machine learning a meta analysis and systematic review |
| url | http://dx.doi.org/10.1155/2022/1734327 |
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