Your browser doesn't support javascript.
loading
Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review.
Șalgau, Cristiana Adina; Morar, Anca; Zgarta, Andrei Daniel; Ancuța, Diana-Larisa; Radulescu, Alexandros; Mitrea, Ioan Liviu; Tanase, Andrei Ovidiu.
Afiliación
  • Șalgau CA; University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania.
  • Morar A; National University of Science and Technology Politehnica Bucharest, Bucharest, Romania. anca.morar@cs.pub.ro.
  • Zgarta AD; Minerva University, San Francisco, USA.
  • Ancuța DL; University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania.
  • Radulescu A; Cantacuzino National Medical-Military Institute for Research and Development, Bucharest, Romania.
  • Mitrea IL; University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania.
  • Tanase AO; University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania.
Ann Biomed Eng ; 52(9): 2348-2371, 2024 Sep.
Article en En | MEDLINE | ID: mdl-38884831
ABSTRACT
Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Periodoncia / Aprendizaje Automático Límite: Humans Idioma: En Revista: Ann Biomed Eng Año: 2024 Tipo del documento: Article País de afiliación: Rumanía Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Periodoncia / Aprendizaje Automático Límite: Humans Idioma: En Revista: Ann Biomed Eng Año: 2024 Tipo del documento: Article País de afiliación: Rumanía Pais de publicación: Estados Unidos