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Artificial Intelligence for Caries Detection: Value of Data and Information.

F Schwendicke1, J Cejudo Grano de Oro1, A Garcia Cantu1

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

Journal of Dental Research
|August 23, 2022
PubMed
Summary

Training artificial intelligence (AI) for dental caries detection with more data improved accuracy and cost-effectiveness. Increasing the training dataset from 10% to 25% yielded the most significant improvements, suggesting data size is crucial for AI development.

Keywords:
AIcaries detection/diagnosis/preventioncomputer simulationdental informaticseconomic evaluationradiology

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Area of Science:

  • * Dental diagnostics and artificial intelligence (AI).
  • * Health economics and cost-effectiveness analysis.
  • * Medical imaging and machine learning.

Background:

  • * Artificial intelligence (AI) shows potential to enhance diagnostic accuracy in dentistry, potentially leading to improved treatment decisions and reduced costs.
  • * Uncertainty persists regarding the cost-effectiveness of AI in clinical practice, particularly for tasks like caries detection.
  • * The impact of training data size on AI performance and economic outcomes requires further investigation.

Purpose of the Study:

  • * To evaluate how increasing the training dataset size for an AI model affects its cost-effectiveness in detecting dental caries on bitewing radiographs.
  • * To determine the value of information gained by reducing uncertainty in key parameters, including AI costs and patient caries risk.
  • * To optimize the development and application of AI in dental diagnostics.

Main Methods:

  • * A convolutional neural network (CNN) was trained using 10%, 25%, 50%, and 100% of a labeled dataset of 49,771 teeth (29,011 without caries, 19,760 with caries).
  • * Health economic modeling, including a Markov model and Monte Carlo microsimulations, was used to quantify cost-effectiveness from a mixed public-private payer perspective in Germany.
  • * The primary health outcome measured was tooth retention years, assessing a 12-year-old individual over their lifetime.

Main Results:

  • * Increasing the AI training data size nonlinearly improved sensitivity and specificity; the largest gains in accuracy and cost-effectiveness occurred when increasing the dataset from 10% to 25%.
  • * In the base-case scenario, AI demonstrated superior effectiveness (mean 62.8 tooth retention years) and lower cost (378 euros) compared to dentists without AI (60.4 years; 419 euros), though with significant uncertainty.
  • * Reducing uncertainty around the population's caries risk profile offered greater economic value than reducing uncertainty related to AI accuracy or costs.

Conclusions:

  • * Informed decisions regarding training data set size are recommended when developing dental AI for caries detection to optimize performance and cost-effectiveness.
  • * The study highlights the significant impact of data quantity on AI diagnostic accuracy and economic outcomes in dentistry.
  • * Further research into the individualized application of AI for caries detection is warranted to maximize cost-effectiveness and clinical utility.