In a 2025 study, Eva Paddenberg-Schubert and her team applied machine learning—including Random Forest, CART, and GLM—to cephalometric data from German orthodontic patients. Their models achieved up to 0.99 accuracy in distinguishing skeletal class I from III, demonstrating the benefits of AI-driven diagnostics in clinical practice.
Q&A
- What is cephalometric analysis?
- How do machine learning models improve diagnosis?
- Why use multiple machine learning models in this study?