Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress - Martins, Mónica Vieira (2023)
first_pagesettingsOrder Article Reprints Open AccessReview Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress by Mónica Vieira Martins 1,*ORCID,Luís Baptista 1ORCID,Henrique Luís 1,2,3,4,Victor Assunção 1,2,3,4,Mário-Rui Araújo 1ORCID andValentim Realinho 1,5ORCID 1 Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal 2 Faculdade de Medicina Dentária, Universidade de Lisboa, Unidade de Investigação em Ciências Orais e Biomédicas (UICOB), Rua Professora Teresa Ambrósio, 1600-277 Lisboa, Portugal 3 Faculdade de Medicina Dentária, Universidade de Lisboa, Rede de Higienistas Orais para o Desenvolvimento da Ciência (RHODes), Rua Professora Teresa Ambrósio, 1600-277 Lisboa, Portugal 4 Center for Innovative Care and Health Technology (ciTechcare), Polytechnic of Leiria, 2410-541 Leiria, Portugal 5 VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal * Author to whom correspondence should be addressed. Computation 2023, 11(6), 115; https://doi.org/10.3390/computation11060115 Submission received: 8 May 2023 / Revised: 5 June 2023 / Accepted: 8 June 2023 / Published: 10 June 2023 (This article belongs to the Special Issue Computational Medical Image Analysis) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of clinical dental images. The present work aims to investigate recent progress concerning the application of ML in the diagnosis of oral diseases using oral X-ray imaging, namely the quality and outcome of such methods. The specific research question was developed using the PICOT methodology. The review was conducted in the Web of Science, Science Direct, and IEEE Xplore databases, for articles reporting the use of ML and AI for diagnostic purposes in X-ray-based oral imaging. Imaging types included panoramic, periapical, bitewing X-ray images, and oral cone beam computed tomography (CBCT). The search was limited to papers published in the English language from 2018 to 2022. The initial search included 104 papers that were assessed for eligibility. Of these, 22 were included for a final appraisal. The full text of the articles was carefully analyzed and the relevant data such as the clinical application, the ML models, the metrics used to assess their performance, and the characteristics of the datasets, were registered for further analysis. The paper discusses the opportunities, challenges, and limitations found.