Tania Camila Niño-Sandoval, Ana Milena Doria-Martinez, Ruby Amparo Vásquez Escobar, Elizabeth Llano Sánchez, Isabella Bermón Rojas, Laura Cristina Vargas Álvarez, David Stephen Fernandez Mc Cann, Liliana Marcela Támara-Patiño
{"title":"Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review.","authors":"Tania Camila Niño-Sandoval, Ana Milena Doria-Martinez, Ruby Amparo Vásquez Escobar, Elizabeth Llano Sánchez, Isabella Bermón Rojas, Laura Cristina Vargas Álvarez, David Stephen Fernandez Mc Cann, Liliana Marcela Támara-Patiño","doi":"10.1007/s00414-024-03162-x","DOIUrl":null,"url":null,"abstract":"<p><p>The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.</p>","PeriodicalId":14071,"journal":{"name":"International Journal of Legal Medicine","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Legal Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00414-024-03162-x","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/24 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MEDICINE, LEGAL","Score":null,"Total":0}
引用次数: 0
Abstract
The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.
期刊介绍:
The International Journal of Legal Medicine aims to improve the scientific resources used in the elucidation of crime and related forensic applications at a high level of evidential proof. The journal offers review articles tracing development in specific areas, with up-to-date analysis; original articles discussing significant recent research results; case reports describing interesting and exceptional examples; population data; letters to the editors; and technical notes, which appear in a section originally created for rapid publication of data in the dynamic field of DNA analysis.