Application of neural networks for the detection of oral cancer: A systematic review.

Journal: Dental and medical problems
Published Date: Jan 1, 2024

Abstract

One potential application of neural networks (NNs) is the early-stage detection of oral cancer. This systematic review aimed to determine the level of evidence on the sensitivity and specificity of NNs for the detection of oral cancer, following the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) and Cochrane guidelines. Literature sources included PubMed, ClinicalTrials, Scopus, Google Scholar, and Web of Science. In addition, the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to assess the risk of bias and the quality of the studies. Only 9 studies fully met the eligibility criteria. In most studies, NNs showed accuracy greater than 85%, though 100% of the studies presented a high risk of bias, and 33% showed high applicability concerns. Nonetheless, the included studies demonstrated that NNs were useful in the detection of oral cancer. However, studies of higher quality, with an adequate methodology, a low risk of bias and no applicability concerns are required so that more robust conclusions could be reached.

Authors

  • María Del Pilar Beristain-Colorado
    Department of Biosciences, Postgraduate Division, Faculty of Medicine, Benito Juárez Autonomous University of Oaxaca, Oaxaca de Juárez, Mexico.
  • María Eugenia Marcela Castro-Gutiérrez
    Department of Biosciences, Postgraduate Division, Faculty of Medicine, Benito Juárez Autonomous University of Oaxaca, Oaxaca de Juárez, Mexico.
  • Rafael Torres-Rosas
    Center for Health and Disease Studies, Postgraduate Division, Faculty of Dentistry, Benito Juárez Autonomous University of Oaxaca, Oaxaca de Juárez, Mexico.
  • Marciano Vargas-Treviño
    Laboratory of Robotics, Bio-Inspired Systems and Artificial Intelligence, Faculty of Biological Systems and Technological Innovations, Benito Juárez Autonomous University of Oaxaca, Oaxaca de Juárez, Mexico.
  • Adriana Moreno-Rodríguez
    Laboratory of Epidemiological and Clinical Studies, Experimental Designs and Research, Faculty of Chemical Sciences, Benito Juárez Autonomous University of Oaxaca, Oaxaca de Juárez, Mexico.
  • Gisela Fuentes-Mascorro
    Laboratory of Animal Reproduction Research (LIRA), Benito Juárez Autonomous University of Oaxaca, Oaxaca de Juárez, Mexico.
  • Liliana Argueta-Figueroa
    National Technological Institute of Mexico/Toluca Institute of Technology (Tecnológico Nacional de México (TecNM)/Instituto Tecnológico de Toluca), Metepec, México.

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