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Artificial Intelligence in Orthodontics: Critical Review.

N F Nordblom1, M Büttner1, F Schwendicke2

  • 1Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Berlin, Germany.

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|April 29, 2024
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Summary
This summary is machine-generated.

Artificial intelligence (AI) aids orthodontists in analyzing complex patient data for treatment planning and decision support. Challenges remain in AI generalizability and standardization for clinical integration.

Keywords:
AIbig datacomputer vision/convolutional neural networksdeep learning/machine learningorthodontic(s)treatment planning

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Area of Science:

  • Orthodontics
  • Digital Health
  • Artificial Intelligence

Background:

  • Digitalization is automating orthodontic manufacturing processes like aligner production.
  • Orthodontic treatment planning and evaluation remain complex, specialist-dependent tasks.
  • Orthodontists utilize longitudinal, multimodal data for individualized treatment decisions.

Purpose of the Study:

  • To review the application of artificial intelligence (AI) in orthodontics.
  • To highlight AI's role in data extraction and decision support for orthodontists.
  • To identify challenges in current AI research and clinical implementation in orthodontics.

Main Methods:

  • Review of AI applications in orthodontic data analysis, including clinical imagery and intraoral scans.
  • Analysis of AI's use in treatment decision support, such as orthognathic surgery or extraction planning.
  • Examination of challenges in AI generalizability, data standardization, and clinical translation.

Main Results:

  • AI assists in automated landmark detection, intraoral scan evaluation, and treatment decision support.
  • Current AI research faces limitations due to unicentric data, bias, and lack of standardized outcomes.
  • Few AI applications have achieved full clinical maturity and regulatory approval.

Conclusions:

  • AI shows promise in enhancing orthodontic data analysis and treatment planning.
  • Addressing challenges in data standardization and generalizability is crucial for AI's clinical adoption.
  • Further research is needed for real-world evaluation and integration of AI into orthodontic workflows.