Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk...

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Detalles Bibliográficos
Autores principales: Hasuike, Akira, Watanabe, Taito, Wakuda, Shin, Kogure, Keisuke, Yanagiya, Ryo, Byrd, Kevin M., Sato, Shuichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605501/
https://www.ncbi.nlm.nih.gov/pubmed/36294820
http://dx.doi.org/10.3390/jpm12101682

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605501/
https://www.ncbi.nlm.nih.gov/pubmed/36294820
http://dx.doi.org/10.3390/jpm12101682

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