Machine learning and deep learning models for the diagnosis of apical periodontitis: a scoping review.

Autor: Basso Á; Facultad de Odontología, Universidad Andres Bello, Santiago, Región Metropolitana, 8370133, Chile., Salas F; Facultad de Odontología, Universidad Andres Bello, Santiago, Región Metropolitana, 8370133, Chile., Hernández M; Laboratorio de Biología Periodontal, Facultad de Odontología, Universidad de Chile, Santiago, 8380544, Chile.; Departamento de Patología y Medicina Oral, Facultad de Odontología, Universidad de Chile, Santiago, 8380544, Chile., Fernández A; Facultad de Odontología, Universidad Andres Bello, Santiago, Región Metropolitana, 8370133, Chile.; Laboratorio de Interacciones Microbianas, Facultad de Odontología, Universidad Andres Bello, Santiago, Región Metropolitana, 8370133, Chile., Sierra A; Facultad de Odontología, Universidad Andres Bello, Santiago, Región Metropolitana, 8370133, Chile. alf.sierra@uandresbello.edu.; Laboratorio de Biología Periodontal, Facultad de Odontología, Universidad de Chile, Santiago, 8380544, Chile. alf.sierra@uandresbello.edu., Jiménez C; Facultad de Odontología, Universidad Andres Bello, Santiago, Región Metropolitana, 8370133, Chile. c.jimenezlizama@uandresbello.edu.
Jazyk: angličtina
Zdroj: Clinical oral investigations [Clin Oral Investig] 2024 Oct 18; Vol. 28 (11), pp. 600. Date of Electronic Publication: 2024 Oct 18.
DOI: 10.1007/s00784-024-05989-5
Abstrakt: Objectives: To assess the existing literature on the use of machine learning (ML) and deep learning (DL) models for diagnosing apical periodontitis (AP) in humans.
Materials and Methods: A scoping review was conducted following the Arksey and O'Malley framework. The PubMed, SCOPUS, and Web of Science databases were searched, focusing on articles using ML/DL approaches for AP diagnosis. No restrictions were applied. Two independent reviewers screened publications and charted data in predefined Excel tables for analysis.
Results: Nineteen publications focused on diagnosing AP by identifying periapical radiolucent lesions (PRLs) in dental radiographs were included. The average sensitivity and specificity for reviewed models were 83% and 90%, respectively. Only three studies explored the direct impact of artificial intelligence (AI) assistance on clinicians' diagnostic performance. Both consistently showed improved sensitivity without compromising specificity. Significant variability in dataset sizes, labeling techniques, and algorithm configurations was noticed.
Conclusions: Findings affirm AI models' effectiveness and transformative potential in diagnosing AP by improving the accurate detection of periapical radiolucencies using dental radiographs. However, the lack of standardized reporting on crucial aspects of methodology and performance metrics prevents establishing a definitive diagnostic approach using AI. Further studies are needed to expand AI applications in AP diagnosis beyond radiographic analysis.
Clinical Relevance: AI can potentially improve diagnostic accuracy in AP diagnosis by enhancing the sensitivity of PRL detection in dental radiographs without compromising specificity.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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