Evaluation of an Artificial Intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs
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2022
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Elsevier
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M. Bonfanti-Gris, A. Garcia-Cañas, R. Alonso-Calvo, M.P. Salido Rodriguez-Manzaneque, G. Pradies Ramiro, Evaluation of an Artificial Intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs, Journal of Dentistry 126 (2022) 104301. https://doi.org/10.1016/j.jdent.2022.104301.
Abstract
Objectives: To evaluate the diagnostic reliability of a web-based Artificial Intelligence program on the detection and classification of dental structures and treatments present on panoramic radiographs.
Methods: A total of 300 orthopantomographies (OPG) were randomly selected for this study. First, the images were visually evaluated by two calibrated operators with radiodiagnosis experience that, after consensus, established the “ground truth”. Operators’ findings on the radiographs were collected and classified as follows: metal restorations (MR), resin-based restorations (RR), endodontic treatment (ET), Crowns (C) and Implants (I). The orthopantomographies were then anonymously uploaded and automatically analyzed by the web-based software (Denti.Ai). Results were then stored, and a statistical analysis was performed by comparing them with the ground truth in terms of Sensitivity (S), Specificity (E), Positive Predictive Value (PPV) Negative Predictive Value (NPV) and its later representation in the area under (AUC) the Receiver Operating Characteristic (ROC) Curve.
Results: Diagnostic metrics obtained for each study variable were as follows: (MR) S=85.48%, E=87.50%, PPV=82.8%, NPV=42.51%, AUC=0.869; (PR) S=41.11%, E=93.30%, PPV=90.24%, NPV=87.50%, AUC=0.672; (ET) S=91.9%, E=100%, PPV=100%, NPV=94.62%, AUC=0.960; (C) S=89.53%, E=95.79%, PPV=89.53%, NPV=95.79%, AUC=0.927; (I) S, E, PPV, NPV=100%, AUC=1.000.
Conclusions: Findings suggest that the web-based Artificial intelligence software provides a good performance on the detection of implants, crowns, metal fillings and endodontic treatments, not being so accurate on the classification of dental structures or resin-based restorations. Clinical Significance: General diagnostic and treatment decisions using orthopantomographies can be improved by using web-based artificial intelligence tools, avoiding subjectivity and lapses from the clinician.
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CRUE-CSIC (Acuerdos Transformativos 2022)