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.
Purpose
The purpose of this study was to systematically critique the digital methods and techniques
used to deploy automated diagnostic tools in the clinical evaluation of altered functional
and parafunctional occlusion.
Material and methods
Articles were screened by 2 reviewers in mid-2022 according to the Preferred Reporting
Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible articles
were critically appraised by using the Joanna Briggs Institute’s Diagnostic Test Accuracy
(JBI-DTA) protocol and Minimum Information for Clinical Artificial Intelligence Modeling
(MI-CLAIM) checklist.
Results
Sixteen articles were extracted. Variations in mandibular anatomic landmarks obtained
via radiographs and photographs produced notable errors in prediction accuracy. While
half of the studies adhered to robust methods of computer science, the lack of blinding
to a reference standard and convenient exclusion of data in favor of accurate machine
learning suggested that conventional diagnostic test methods were ineffective in regulating
machine learning research in clinical occlusion. As preestablished baselines or criterion
standards were lacking for model evaluation, a heavy reliance was placed on the validation
provided by clinicians, often dental specialists, which was prone to subjective biases
and largely governed by professional experience.
Conclusions
Based on the findings and because of the numerous clinical variables and inconsistencies,
the current literature on dental machine learning presented nondefinitive but promising
results in diagnosing functional and parafunctional occlusal parameters.
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Journal of Prosthetic DentistryAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Developments, application, and performance of artificial intelligence in dentistry–a systematic review.J Dent Sci. 2020; 16: 508-522
- Machine learning and intelligent diagnostics in dental and orofacial pain management: A systematic review.Pain Res Manag. 2021; 2021: 6659133
- Mastication evaluation with unsupervised learning: using an inertial sensor-based system.IEEE J Transl Eng Health Med. 2018; 6: 1-10
- Nomenclature and classification of temporomandibular joint disorders.J Oral Rehabil. 2010; 37: 760-765
- Virtual articulators and virtual mounting procedures: where do we stand?.J Prosthodont. 2021; 30: 24-35
- Variables influencing the device-dependent approaches in digitally analysing jaw movement—a systematic review.Clin Oral Investig. 2022; 27: 1-16
- A review of medical image data augmentation techniques for deep learning applications.J Med Imaging Radiat Oncol. 2021; 65: 545-563
- Machine learning in the prognostic appraisal of Class III growth.in: Seminars in Orthodontics. Elsevier, 2021: 96-108
- Physiologic vs. pathologic occlusion.J Am Dent Assoc. 1938; 25: 1583-1594
- Constructing a search strategy and searching for evidence.Am J Nurs. 2014; 114: 49-56
- Plagiarism in dentistry - a systematic review.Br Dent J. 20 October 2020; ([Epub ahead of print.])https://doi.org/10.1038/s41415-020-2026-4
- Functional occlusion in restorative dentistry and prosthodontics.Elsevier Health Sciences, 2015
- The Glossary of Prosthodontic Terms: Ninth Edition.J Prosthet Dent. 2017; 117: e1-105
- Critical Appraisal Tools - Joanna Briggs Institute [Internet].(Available at:)https://jbi.global/critical-appraisal-toolsDate accessed: February 14, 2023
- Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.Nat Med. 2020; 26: 1320-1324
- Surgery-First Orthognathic Approach to Correct Facial Asymmetry: Artificial Intelligence–Based Cephalometric Analysis.Plast Reconstr Surg. 2022; 149: 496e-499e
- Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals.BMC Oral Health. 2021; 21: 1-7
- Deep-learning-based detection of cranio-spinal differences between skeletal classification using cephalometric radiography.Diagnostics. 2021; 11: 591
- Use of artificial intelligence to predict outcomes of nonextraction treatment of Class II malocclusions.in: Seminars in Orthodontics. Elsevier, 2021: 87-95
- Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III.Forensic Sci Int. 2017; 281: 187.e1-187.e7
- Electronic cephalometric diagnosis: Contextualized cephalometric variables.Dental Press J Orthod. 2011; 16: 75-84
- Artificial intelligence analysis of mandibular movements enables accurate detection of phasic sleep bruxism in OSA patients: a pilot study.Nat Sci Sleep. 2021; 13: 1449-1459
- Identification of arthropathy and myopathy of the temporomandibular syndrome by biomechanical facial features.Biomed Eng Online. 2020; 19: 1-18
- Bayesian-based decision support system for assessing the needs for orthodontic treatment.Healthc Inform Res. 2018; 24: 22-28
- An artificial neural network model for the prediction of bruxism by means of occlusal variables.Neural Comput Appl. 2020; 32: 1259-1267
- Improving the video imaging prediction of postsurgical facial profiles with an artificial neural network.J Dent Sci. 2009; 4: 118-129
- Machine learning in orthodontics: Automated facial analysis of vertical dimension for increased precision and efficiency.Am J Orthod Dentofacial Orthop. 2022; 161: 445-450
- The validity of an artificial intelligence application for assessment of orthodontic treatment need from clinical images.in: Seminars in Orthodontics. Elsevier, 2021: 164-171
- DCPR-GAN: dental crown prosthesis restoration using two-stage generative adversarial networks.IEEE J Biomed Health Inform. 2021; 26: 151-160
- OCLU-NET for occlusal classification of 3D dental models.Mach Vis Appl. 2020; 31: 1-12
Article info
Publication history
Published online: February 17, 2023
Publication stage
In Press Corrected ProofFootnotes
Funding: Supported by the University of Adelaide Kwok Paul Lee Bequest Project ID 75131603.
Identification
Copyright
© 2023 by the Editorial Council for the Journal of Prosthetic Dentistry.
