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Impact of haptic simulators in preclinical dental education: A systematic review
Octave Nadile Bandiaky1, Serena Lopez1,2, Ludovic Hamon3
1Nantes Université, Oniris, Univ Angers, CHU Nantes, INSERM, Regenerative Medicine and Skeleton, RMeS, Nantes, France.
Background:
Haptic technologies have opened a new avenue in preclinical dental education, with evidence that they can be used to improve student performance. The aim of this systematic review was to (1) determine the effect of haptic simulators on motor skill acquisition during preclinical dental training, (2) explore students' perception, and (3) explore the ability of haptic systems to distinguish users based on their initial level of manual dexterity.
Methods:
A comprehensive search of articles published up to February 2023 was performed using five databases (i.e., PubMed/Medline, ScienceDirect, Web of Sciences, Scopus, and Cochrane Library) and specialized journals. The Preferred Reporting Items for Systematic Review and Meta-Analysis 2020 guidelines were followed, and the risk of bias was assessed. Only studies on the application of haptic simulators in dentistry preclinical training were included. Qualitative synthesis of data was performed, and the protocol was registered in PROSPERO (ID = CRD42022337177).
Results:
Twenty-three clinical studies, including 1303 participants, were included. The authors observed a statistically significant improvement in dental students' motor skills in various dental specialties, such as restorative dentistry, pediatric, prosthodontics, periodontics, implantology, and dental surgery, after haptic training. Haptic technologies were perceived well by all participants, with encouraging data regarding their ability to differentiate users according to their initial level of manual dexterity.
Conclusions:
Our work suggests that haptic simulators can significantly improve motor skill acquisition in preclinical dental training. This new digital technology, which was well perceived by the participants, also showed encouraging results in discriminating users according to their level of experience.
