Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography.
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...
| Publicado en: | BMC Oral Health Vol. 25; no. 1; pp. 1 - 15 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
BioMed Central
6/6/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185781510&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185781510 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726831 1CIC jtl: BMC Oral Health issn: 14726831 maglogo: N pubinfo: dt: 6/6/2025 vid: 25 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 185781510 185781510 185781510 10.1186/s12903-025-06293-8 185781510 ppf: 1 ppct: 14 formats: tig: atl: Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography. aug: au: Ma, Yujie Al-Aroomi, Maged Ali Zheng, Yutian Ren, Wenjie Liu, Peixuan Wu, Qing Liang, Ye Jiang, Canhua affil: https://ror.org/05c1yfj14 Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, 410008, Changsha, Hunan Province, China sug: subj: Deep Learning Algorithms Diagnosis, Computer Assisted Dental Caries Radiography Tomography, X-Ray Computed Radiography, Dental Human Funding Source Automation Sensitivity and Specificity Predictive Value of Tests Descriptive Statistics Convolutional Neural Networks ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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