Abstract:
Aim
The study aims to assess the success of an AI system based on the deep convolutional neural network (D-CNN) for segmenting masseter muscles on ultrasonography (USG) images.
Materials and Methods
- Study Design: Retrospective study using 195 anonymized USG images from the radiology archive of the Faculty of Dentistry, Ankara University.
- Technology: Utilized U-net, Pyramid Scene Parsing Network (PSPNet), and Fuzzy Petri Net (FPN) architectures for deep learning.
- Comparison: Manual segmentation and measurements were statistically compared with AI results.
- Statistical Analysis: Accuracy, ROC AUC, and PRC AUC were calculated and compared between human observers and the AI model using the Mann–Whitney U test.
Results
- AI models (FPN, PSPNet, U-net) demonstrated high accuracy in detecting and segmenting masseter muscles.
- The D-CNN measurements were consistent with manual measurements, showing no significant differences.
Conclusion: The AI system is promising for automatic masseter muscle segmentation and thickness measurement on USG images, potentially aiding professionals in diagnosis and saving time.
Introduction
- Bruxism, affecting 8% to 31% of adults, leads to masseter muscle issues such as inflammation and hypertrophy.
- Ultrasonography (USG) is a key diagnostic tool for muscle assessment, but variability in results due to operator experience is a challenge.
- AI and deep learning offer potential improvements in accuracy and efficiency for medical imaging.
Materials and Methods:
Data Collection
- USG images of 24 patients diagnosed with bruxism were collected.
- A total of 195 images were used, divided into training (157), validation (18), and test (20) groups.
Image Annotation
- Manual annotations by an experienced radiologist were used as ground truth.
- AI architectures employed included U-net, PSPNet, and FPN.
Model Training and Evaluation
- AI models were trained to detect and segment masseter muscles.
- Performance metrics such as accuracy, sensitivity, specificity, precision, F1-score, ROC AUC, and PRC AUC were calculated.
Results
- The AI models, particularly FPN and U-net, showed high accuracy (0.985 and 0.969 respectively).
- No significant difference was found between AI and manual measurements (P > .05).
Discussion
- AI systems show potential in improving diagnostic accuracy and efficiency for masseter muscle assessment in bruxism patients.
- The findings align with previous research on AI's effectiveness in medical imaging.
Conclusion
The AI-based approach for USG image analysis is effective for automatic masseter muscle segmentation, aiding in diagnosis and potentially enhancing clinical workflows.
