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Development and Performance of an Artificial Intelligence-Based Deep Learning Model Designed for Evaluating Dental

Sanjeev B Khanagar1,2, Aram Alshehri2,3, Farraj Albalawi1,2

  • 1Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia.

Healthcare (Basel, Switzerland)
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Summary

An AI model accurately assesses dental ergonomics, outperforming human specialists in identifying poor postures. This technology offers real-time feedback to prevent musculoskeletal issues for dental professionals.

Keywords:
artificial intelligenceassessmentautomateddeep learningdental postureergonomicsevaluationmusculoskeletal disorderspreventionwork postures

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Occupational Health

Background:

  • Dental professionals face challenging workspaces leading to poor postures and musculoskeletal disorders.
  • These issues cause absenteeism, reduced productivity, and premature retirement in the dental workforce.
  • Developing objective tools to assess dental ergonomics is crucial for workforce well-being.

Purpose of the Study:

  • To develop and evaluate an Artificial Intelligence (AI)-based deep learning model for assessing dental ergonomic postures.
  • The model, SBK-DentErgo, aims to provide an objective and reliable method for posture evaluation.
  • To compare the AI model's performance against human expert evaluation.

Main Methods:

  • Developed an AI model integrating YOLOv11 and MediaPipe for posture assessment.
  • Trained and validated the model using 500 photographs of dental professionals in various procedures.
  • Compared AI model's assessments with those of calibrated evaluators and dental specialists.

Main Results:

  • The AI model demonstrated excellent agreement with calibrated evaluators (Kappa = 0.922) and high reliability (ICC = 1.000).
  • AI achieved significantly higher sensitivity (97%) and specificity (85.7%) compared to human evaluation (20.5% sensitivity, 9.1% specificity).
  • The AI model's performance suggests it could serve as a 'gold standard' for evaluating dental operator ergonomics (AUC = 0.917).

Conclusions:

  • The AI-based Dental Ergonomic Posture Assessment Model shows exceptional performance in evaluating dental professional postures.
  • The model surpasses experienced specialists in sensitivity and specificity, offering objective ergonomic assessments.
  • Real-time feedback from the AI enables immediate self-correction, potentially preventing long-term postural problems.