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Background <p>Identifying fractured endodontic instruments (FEIs) in periapical radiographs (PAs) is a critical yet challenging aspect of root canal treatment (RCT) due to anatomical complexities and overlapping structures. Deep learning (DL) models offer potential solutions, yet their comparative performance in this domain remains underexplored.</p> Methods <p>A dataset of 700 annotated PAs, including 381 teeth with FEIs, was divided into training, validation, and test sets (60/20/20 split). Five DL models—DenseNet201, EfficientNet B0, ResNet-18, VGG-19, and MaxVit-T—were trained using transfer learning and data augmentation techniques. Performance was evaluated using accuracy, AUC and MCC. Statistical analysis included the Friedman test with post-hoc corrections.</p> Results <p>DenseNet201 achieved the highest AUC (0.900) and MCC (0.810), outperforming other models in FEI detection. ResNet-18 demonstrated robust results, while EfficientNet B0 and VGG-19 provided moderate performance. MaxVit-T underperformed, with metrics near random guessing. Statistical analysis revealed significant differences among models (<i>p</i> &lt; 0.05), but pairwise comparisons were not significant.</p> Conclusions <p>DenseNet201’s superior performance highlights its clinical potential for FEI detection, while ResNet-18 offers a balance between accuracy and computational efficiency. The findings highlight the need for model-task alignment and optimization in medical imaging applications.</p>

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Deep learning algorithms for detecting fractured instruments in root canals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月23日

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  • Ekin Deniz Çatmabacak,
  • İrem Çetinkaya

摘要

Background

Identifying fractured endodontic instruments (FEIs) in periapical radiographs (PAs) is a critical yet challenging aspect of root canal treatment (RCT) due to anatomical complexities and overlapping structures. Deep learning (DL) models offer potential solutions, yet their comparative performance in this domain remains underexplored.

Methods

A dataset of 700 annotated PAs, including 381 teeth with FEIs, was divided into training, validation, and test sets (60/20/20 split). Five DL models—DenseNet201, EfficientNet B0, ResNet-18, VGG-19, and MaxVit-T—were trained using transfer learning and data augmentation techniques. Performance was evaluated using accuracy, AUC and MCC. Statistical analysis included the Friedman test with post-hoc corrections.

Results

DenseNet201 achieved the highest AUC (0.900) and MCC (0.810), outperforming other models in FEI detection. ResNet-18 demonstrated robust results, while EfficientNet B0 and VGG-19 provided moderate performance. MaxVit-T underperformed, with metrics near random guessing. Statistical analysis revealed significant differences among models (p < 0.05), but pairwise comparisons were not significant.

Conclusions

DenseNet201’s superior performance highlights its clinical potential for FEI detection, while ResNet-18 offers a balance between accuracy and computational efficiency. The findings highlight the need for model-task alignment and optimization in medical imaging applications.