Olga Di Fede, Gaetano La Mantia, Marco Parola, Laura Maniscalco, Domenica Matranga, Pietro Tozzo, Giuseppina Campisi, Mario G C A Cimino
{"title":"利用深度学习自动检测口腔恶性病变：范围综述与元分析》。","authors":"Olga Di Fede, Gaetano La Mantia, Marco Parola, Laura Maniscalco, Domenica Matranga, Pietro Tozzo, Giuseppina Campisi, Mario G C A Cimino","doi":"10.1111/odi.15188","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classification of oral lesions. This research, by conducting a scoping review and meta-analysis, aims to provide an overview of the progress and achievements in the field of automated detection of oral lesions using DL.</p><p><strong>Materials and methods: </strong>A scoping review was conducted to identify relevant studies published in the last 5 years (2018-2023). A comprehensive search was conducted using several electronic databases, including PubMed, Web of Science, and Scopus. Two reviewers independently assessed the studies for eligibility and extracted data using a standardized form, and a meta-analysis was conducted to synthesize the findings.</p><p><strong>Results: </strong>Fourteen studies utilizing various DL algorithms were identified and included for the detection and classification of oral lesions from clinical images. Among these, three were included in the meta-analysis. The estimated pooled sensitivity and specificity were 0.86 (95% confidence interval [CI] = 0.80-0.91) and 0.67 (95% CI = 0.58-0.75), respectively.</p><p><strong>Conclusions: </strong>The results of meta-analysis indicate that DL algorithms improve the diagnosis of oral lesions. Future research should develop validated algorithms for automated diagnosis.</p><p><strong>Trial registration: </strong>Open Science Framework (https://osf.io/4n8sm).</p>","PeriodicalId":19615,"journal":{"name":"Oral diseases","volume":" ","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.\",\"authors\":\"Olga Di Fede, Gaetano La Mantia, Marco Parola, Laura Maniscalco, Domenica Matranga, Pietro Tozzo, Giuseppina Campisi, Mario G C A Cimino\",\"doi\":\"10.1111/odi.15188\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classification of oral lesions. This research, by conducting a scoping review and meta-analysis, aims to provide an overview of the progress and achievements in the field of automated detection of oral lesions using DL.</p><p><strong>Materials and methods: </strong>A scoping review was conducted to identify relevant studies published in the last 5 years (2018-2023). A comprehensive search was conducted using several electronic databases, including PubMed, Web of Science, and Scopus. Two reviewers independently assessed the studies for eligibility and extracted data using a standardized form, and a meta-analysis was conducted to synthesize the findings.</p><p><strong>Results: </strong>Fourteen studies utilizing various DL algorithms were identified and included for the detection and classification of oral lesions from clinical images. Among these, three were included in the meta-analysis. The estimated pooled sensitivity and specificity were 0.86 (95% confidence interval [CI] = 0.80-0.91) and 0.67 (95% CI = 0.58-0.75), respectively.</p><p><strong>Conclusions: </strong>The results of meta-analysis indicate that DL algorithms improve the diagnosis of oral lesions. Future research should develop validated algorithms for automated diagnosis.</p><p><strong>Trial registration: </strong>Open Science Framework (https://osf.io/4n8sm).</p>\",\"PeriodicalId\":19615,\"journal\":{\"name\":\"Oral diseases\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Oral diseases\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/odi.15188\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oral diseases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/odi.15188","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.
Objective: Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classification of oral lesions. This research, by conducting a scoping review and meta-analysis, aims to provide an overview of the progress and achievements in the field of automated detection of oral lesions using DL.
Materials and methods: A scoping review was conducted to identify relevant studies published in the last 5 years (2018-2023). A comprehensive search was conducted using several electronic databases, including PubMed, Web of Science, and Scopus. Two reviewers independently assessed the studies for eligibility and extracted data using a standardized form, and a meta-analysis was conducted to synthesize the findings.
Results: Fourteen studies utilizing various DL algorithms were identified and included for the detection and classification of oral lesions from clinical images. Among these, three were included in the meta-analysis. The estimated pooled sensitivity and specificity were 0.86 (95% confidence interval [CI] = 0.80-0.91) and 0.67 (95% CI = 0.58-0.75), respectively.
Conclusions: The results of meta-analysis indicate that DL algorithms improve the diagnosis of oral lesions. Future research should develop validated algorithms for automated diagnosis.
Trial registration: Open Science Framework (https://osf.io/4n8sm).
期刊介绍：
Oral Diseases is a multidisciplinary and international journal with a focus on head and neck disorders, edited by leaders in the field, Professor Giovanni Lodi (Editor-in-Chief, Milan, Italy), Professor Stefano Petti (Deputy Editor, Rome, Italy) and Associate Professor Gulshan Sunavala-Dossabhoy (Deputy Editor, Shreveport, LA, USA). The journal is pre-eminent in oral medicine. Oral Diseases specifically strives to link often-isolated areas of dentistry and medicine through broad-based scholarship that includes well-designed and controlled clinical research, analytical epidemiology, and the translation of basic science in pre-clinical studies. The journal typically publishes articles relevant to many related medical specialties including especially dermatology, gastroenterology, hematology, immunology, infectious diseases, neuropsychiatry, oncology and otolaryngology. The essential requirement is that all submitted research is hypothesis-driven, with significant positive and negative results both welcomed. Equal publication emphasis is placed on etiology, pathogenesis, diagnosis, prevention and treatment.
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