Clinical machine learning in parafunctional and altered functional occlusion: A systematic review.

Journal: The Journal of prosthetic dentistry
PMID: 36801145

Abstract

STATEMENT OF PROBLEM: The advent of machine learning in the complex subject of occlusal rehabilitation warrants a thorough investigation into the techniques applied for successful clinical translation of computer automation. A systematic evaluation on the topic with subsequent discussion of the clinical variables involved is lacking.

Authors

  • Taseef Hasan Farook
    Maxillofacial Prosthetic Service, Prosthodontic Unit, School of Dental Sciences, UniversitiSains Malaysia, Health Campus, Kelantan 16150, Malaysia.
  • Farah Rashid
    Researcher, Adelaide Dental School, The University of Adelaide, South Australia, Australia.
  • Saif Ahmed
    Lecturer, Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
  • James Dudley
    Associate Professor, Adelaide Dental School, The University of Adelaide, South Australia, Australia.

Keywords

External Resources

Popular Topics
Recent Journals