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Deep learning algorithms for detecting fractured instruments in root canals
Ekin Deniz Çatmabacak1, İrem Çetinkaya2
1Department of Endodontics, Trakya University, Balkan Campus, Edirne, Turkey.
BMC Oral Health
|February 23, 2025
Summary
DenseNet201 demonstrated superior performance in detecting fractured endodontic instruments (FEIs) on periapical radiographs. This deep learning model shows clinical potential for improving root canal treatment outcomes.
Area of Science:
- Dentistry
- Medical Imaging
- Artificial Intelligence
Background:
- Identifying fractured endodontic instruments (FEIs) in periapical radiographs (PAs) is crucial but challenging in root canal treatment (RCT).
- Deep learning (DL) models present a potential solution, but comparative performance analysis is lacking.
Purpose of the Study:
- To evaluate and compare the performance of five deep learning models for detecting FEIs in PAs.
- To identify the most effective DL model for FEI detection in endodontics.
Main Methods:
- A dataset of 700 annotated PAs (381 with FEIs) was used for training, validation, and testing.
- Five DL models (DenseNet201, EfficientNet B0, ResNet-18, VGG-19, MaxVit-T) were trained using transfer learning and data augmentation.
- Performance was assessed using accuracy, AUC, and MCC, with statistical analysis via Friedman test.
Main Results:
- DenseNet201 achieved the highest AUC (0.900) and MCC (0.810) for FEI detection.
- ResNet-18 showed robust results, while EfficientNet B0 and VGG-19 had moderate performance.
- MaxVit-T underperformed significantly; statistical analysis indicated significant differences among models (p < 0.05).
Conclusions:
- DenseNet201 exhibits significant clinical potential for accurate FEI detection.
- ResNet-18 offers a good balance of accuracy and computational efficiency.
- Model-task alignment and optimization are critical for medical imaging applications.
