Machine Learning to Predict Apical Lesions: A Cross-Sectional and Model Development Study

(1) Background: We aimed to identify factors associated with the presence of apical lesions (AL) in panoramic radiographs and to evaluate the predictive value of the identified factors. (2) Methodology: Panoramic radiographs from 1071 patients (age: 11–93 a, mean: 50.6 a ± 19.7 a) with 27,532 teeth...

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Detalles Bibliográficos
Autores principales: Herbst, Sascha Rudolf, Pitchika, Vinay, Krois, Joachim, Krasowski, Aleksander, Schwendicke, Falk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10488275/
https://www.ncbi.nlm.nih.gov/pubmed/37685531
http://dx.doi.org/10.3390/jcm12175464

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10488275/
https://www.ncbi.nlm.nih.gov/pubmed/37685531
http://dx.doi.org/10.3390/jcm12175464

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