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Machine Learning in Dentistry: A Scoping Review.

Lubaina T Arsiwala-Scheppach1,2, Akhilanand Chaurasia2,3, Anne Müller4

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This summary is machine-generated.

Machine learning (ML) in dentistry shows rapid growth, but studies often have high bias and poor reporting. Standardized metrics are needed for reliable ML dental research.

Keywords:
dental radiographydentistrymachine learningneural networksscoping review

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Area of Science:

  • Dental research
  • Artificial intelligence
  • Machine learning applications

Background:

  • Machine learning (ML) is increasingly utilized in dental research and clinical applications.
  • A systematic compilation and quality assessment of ML studies in dentistry is lacking.

Purpose of the Study:

  • To systematically review studies employing ML in dentistry.
  • To assess the methodological quality, risk of bias, and reporting standards of these studies.

Main Methods:

  • Systematic literature search on MEDLINE, IEEE Xplore, and arXiv (Jan 2015 - May 2021).
  • Evaluation of publication trends, ML task distribution, and clinical field application.
  • Appraisal of risk of bias using QUADAS-2 and adherence to reporting standards using TRIPOD checklists.

Main Results:

  • 168 studies were included, with classification tasks being most common.
  • A wide variety of ML models, data, and performance metrics (42 types) were used.
  • Considerable risk of bias and moderate adherence to reporting standards were observed, hindering result replication.

Conclusions:

  • The application of machine learning in dentistry is expanding, but methodological rigor is a concern.
  • Inconsistent reporting and high risk of bias impede the reliability and comparability of ML-based dental studies.
  • Establishing a core set of outcomes and metrics is crucial for advancing ML in dentistry.