Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography
Abstract Objectives Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms for detecting and diagnosing dental carie...
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BMC
2025-06-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-06293-8 |
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| author | Yujie Ma Maged Ali Al-Aroomi Yutian Zheng Wenjie Ren Peixuan Liu Qing Wu Ye Liang Canhua Jiang |
| author_facet | Yujie Ma Maged Ali Al-Aroomi Yutian Zheng Wenjie Ren Peixuan Liu Qing Wu Ye Liang Canhua Jiang |
| author_sort | Yujie Ma |
| collection | DOAJ |
| description | Abstract Objectives Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms for detecting and diagnosing dental caries using cone-beam computed tomography (CBCT) with the Mask R-CNN architecture while comparing various hyperparameters to enhance detection. Materials and methods A total of 2,128 CBCT images were divided into training and validation and test datasets in a 7:1:1 ratio. For the verification of tooth recognition, the data from the validation set were randomly selected for analysis. Three groups of Mask R-CNN networks were compared: A scratch-trained baseline using randomly initialized weights (R group); A transfer learning approach with models pre-trained on COCO for object detection (C group); A variant pre-trained on ImageNetfor for object detection (I group). All configurations maintained identical hyperparameter settings to ensure fair comparison. The deep learning model used ResNet-50 as the backbone network and was trained to 300epoch respectively. We assessed training loss, detection and training times, diagnostic accuracy, specificity, positive and negative predictive values, and coverage precision to compare performance across the groups. Results Transfer learning significantly reduced training times compared to non-transfer learning approach (p < 0.05). The average detection time for group R was 0.269 ± 0.176 s, whereas groups I (0.323 ± 0.196 s) and C (0.346 ± 0.195 s) exhibited significantly longer detection times (p < 0.05). C-group, trained for 200 epochs, achieved a mean average precision (mAP) of 81.095, outperforming all other groups. The mAP for caries recognition in group R, trained for 300 epochs, was 53.328, with detection times under 0.5 s. Overall, C-group demonstrated significantly higher average precision across all epochs (100, 200, and 300) (p < 0.05). Conclusion Neural networks pre-trained with COCO transfer learning exhibit superior annotation accuracy compared to those pre-trained with ImageNet. This suggests that COCO’s diverse and richly annotated images offer more relevant features for detecting dental structures and carious lesions. Furthermore, employing ResNet-50 as the backbone architecture enhances the detection of teeth and carious regions, achieving significant improvements with just 200 training epochs, potentially increasing the efficiency of clinical image interpretation. |
| format | Article |
| id | doaj-art-e9f7c9f413e94bab89feda7bb6862d4b |
| institution | OA Journals |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-e9f7c9f413e94bab89feda7bb6862d4b2025-08-20T02:31:09ZengBMCBMC Oral Health1472-68312025-06-0125111410.1186/s12903-025-06293-8Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomographyYujie Ma0Maged Ali Al-Aroomi1Yutian Zheng2Wenjie Ren3Peixuan Liu4Qing Wu5Ye Liang6Canhua Jiang7Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South UniversityDepartment of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South UniversityThe College of Mechanical and Electrical Engineering, Central South UniversityDepartment of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South UniversityDepartment of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South UniversityHigh Performance Computing Center, Central South UniversityDepartment of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South UniversityDepartment of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South UniversityAbstract Objectives Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms for detecting and diagnosing dental caries using cone-beam computed tomography (CBCT) with the Mask R-CNN architecture while comparing various hyperparameters to enhance detection. Materials and methods A total of 2,128 CBCT images were divided into training and validation and test datasets in a 7:1:1 ratio. For the verification of tooth recognition, the data from the validation set were randomly selected for analysis. Three groups of Mask R-CNN networks were compared: A scratch-trained baseline using randomly initialized weights (R group); A transfer learning approach with models pre-trained on COCO for object detection (C group); A variant pre-trained on ImageNetfor for object detection (I group). All configurations maintained identical hyperparameter settings to ensure fair comparison. The deep learning model used ResNet-50 as the backbone network and was trained to 300epoch respectively. We assessed training loss, detection and training times, diagnostic accuracy, specificity, positive and negative predictive values, and coverage precision to compare performance across the groups. Results Transfer learning significantly reduced training times compared to non-transfer learning approach (p < 0.05). The average detection time for group R was 0.269 ± 0.176 s, whereas groups I (0.323 ± 0.196 s) and C (0.346 ± 0.195 s) exhibited significantly longer detection times (p < 0.05). C-group, trained for 200 epochs, achieved a mean average precision (mAP) of 81.095, outperforming all other groups. The mAP for caries recognition in group R, trained for 300 epochs, was 53.328, with detection times under 0.5 s. Overall, C-group demonstrated significantly higher average precision across all epochs (100, 200, and 300) (p < 0.05). Conclusion Neural networks pre-trained with COCO transfer learning exhibit superior annotation accuracy compared to those pre-trained with ImageNet. This suggests that COCO’s diverse and richly annotated images offer more relevant features for detecting dental structures and carious lesions. Furthermore, employing ResNet-50 as the backbone architecture enhances the detection of teeth and carious regions, achieving significant improvements with just 200 training epochs, potentially increasing the efficiency of clinical image interpretation.https://doi.org/10.1186/s12903-025-06293-8CBCTImage recognitionCariesMask R-CNNDeep learning |
| spellingShingle | Yujie Ma Maged Ali Al-Aroomi Yutian Zheng Wenjie Ren Peixuan Liu Qing Wu Ye Liang Canhua Jiang Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography BMC Oral Health CBCT Image recognition Caries Mask R-CNN Deep learning |
| title | Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography |
| title_full | Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography |
| title_fullStr | Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography |
| title_full_unstemmed | Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography |
| title_short | Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography |
| title_sort | application of mask r cnn for automatic recognition of teeth and caries in cone beam computerized tomography |
| topic | CBCT Image recognition Caries Mask R-CNN Deep learning |
| url | https://doi.org/10.1186/s12903-025-06293-8 |
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