Artificial Intelligence in Orthodontics: Part 2-Data Preparation and Performance Evaluation

Area of Science:

• Biomedical Engineering
• Dental Research
• Machine Learning in Healthcare

Background:

• Artificial intelligence (AI) is increasingly used in orthodontics.
• AI system reliability hinges on data quality and rigorous performance evaluation.
• This is Part 2 of a 3-part review focusing on practical implementation.

Purpose of the Study:

• To provide a practical overview of data preparation for orthodontic AI.
• To guide the performance evaluation of AI models in orthodontics.
• To discuss the clinical utility of validated AI systems.

Main Methods:

• Review of data preparation strategies for AI in orthodontics.
• Analysis of performance evaluation metrics for AI models (classification, regression, segmentation).
• Discussion of generalizability, patient privacy, and model robustness.

Main Results:

• Highlights key considerations for creating representative, reproducible, and clinically meaningful datasets.
• Presents strategies for data processing to enhance generalizability and maintain privacy.
• Details strengths and limitations of common performance metrics for AI tasks.

Conclusions:

• Well-prepared data and rigorous evaluation are crucial for reliable orthodontic AI.
• Understanding performance metrics ensures robust AI model validation.
• Trustworthy AI can significantly advance orthodontic diagnostics, treatment planning, and research.