Deep learning for determining the difficulty of endodontic treatment: a pilot study.

Area of Science:

• Artificial Intelligence in Dentistry
• Radiographic Image Analysis
• Machine Learning for Medical Diagnosis

Background:

• Assessing endodontic case difficulty is crucial for treatment planning and outcomes.
• Current methods rely on subjective human evaluation, leading to variability.
• Automating this assessment using artificial intelligence can standardize and improve accuracy.

Purpose of the Study:

• To develop and validate a deep learning model for automated assessment of endodontic case difficulty.
• To compare the performance of deep learning models against human examiners.
• To explore the utility of self-supervised learning for dental radiographic analysis.

Main Methods:

• A dataset of 1,386 periapical radiographs was annotated by dentists and endodontists.
• Convolutional neural networks (VGG16, ResNet, Inception) were trained using transfer learning and self-supervised contrastive learning.
• Models were evaluated using 10-fold cross-validation and compared to seven human examiners.

Main Results:

• The baseline VGG16 model achieved 87.62% accuracy in classifying difficulty.
• Self-supervised pretraining did not enhance model performance.
• All deep learning models outperformed human raters, who exhibited poor inter-examiner reliability.

Conclusions:

• Deep learning models show feasibility for automated endodontic case difficulty assessment.
• AI-driven assessment offers a more objective and reliable alternative to human evaluation.
• Further research can refine these models for clinical integration.