Using machine learning to classify temporomandibular disorders: a proof of concept.

Journal: Journal of applied oral science : revista FOB

PMID: 39504112

Abstract

BACKGROUND: the escalating influx of patients with temporomandibular disorders and the challenges associated with accurate diagnosis by non-specialized dental practitioners underscore the integration of artificial intelligence into the diagnostic process of temporomandibular disorders (TMD) as a potential solution to mitigate diagnostic disparities associated with this condition.

Authors

Fernanda Pretto Zatt

Universidade Federal de Santa Catarina (UFSC), Departamento de Odontologia, Florianópolis, Brasil.
João Victor Cunha Cordeiro

Universidade Federal de Santa Catarina (UFSC), Departamento de Odontologia, Florianópolis, Brasil.
Lauren Bohner

Department of Oral and Maxillofacial Surgery, Hospital University Muenster, 48149 Muenster, Germany.
Beatriz Dulcineia Mendes de Souza

Universidade Federal de Santa Catarina (UFSC), Departamento de Odontologia, Florianópolis, Brasil.
Victor Emanoel Armini Caldas

Independent Researcher, Amsterdam, Netherlands.
Ricardo Armini Caldas

Universidade Federal de Santa Catarina (UFSC), Departamento de Odontologia, Florianópolis, Brasil.

Keywords

Adult Decision Trees Facial Pain Female Humans Machine Learning Male Middle Aged Proof of Concept Study Reproducibility of Results Temporomandibular Joint Disorders Young Adult

External Resources

View on PubMed Access via DOI PubMed (39504112)

Using machine learning to classify temporomandibular disorders: a proof of concept.

Abstract

Authors

Keywords

External Resources

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