Clinical oral investigations · 2024
Detection of C-shaped mandibular second molars on panoramic radiographs using deep convolutional neural networks.
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Jin Long, Zhou Wenyuan, Tang Ying, Yu Zezheng, Fan Juan, Wang Lu, Liu Chao, Gu Yongchun, Zhang Panpan
Аннотация
OBJECTIVES: The C-shaped mandibular second molars (MSMs) may pose an endodontic challenge. The aim of this study was to develop a convolutional neural network (CNN)-based deep learning system for the diagnosis of C-shaped MSMs on panoramic radiographs.
MATERIALS AND METHODS: Panoramic radiographs and cone beam computed tomographic (CBCT) images were collected from a hospital in China and subsequently divided into two groups. In Group A, conventional panoramic images and CBCT images were derived from the same patients (n = 730 individuals), and the dataset consisted of conventional panoramic image patches of 1453 MSMs. In Group B (n = 610 individuals), the patients underwent CBCT examinations in the absence of available panoramic images; CBCT images were acquired and utilized to generate simulated panoramic images, and the dataset consisted of image patches of 1211 MSMs. Five pretrained CNN networks (ResNet-101 and - 50, DenseNet-121 and - 161, and Inception-V3) were utilized for the classification of C-shaped and non-C-shaped MSMs. Finally, the networks trained on the Group B dataset were tested on the Group A dataset. The diagnostic performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis, and the CBCT images were taken as the gold standard. The results were compared with those achieved by three dental professionals.
RESULTS: In Group A, all five networks exhibited satisfactory diagnostic performance, with AUC values ranging from 0.875 to 0.916 and accuracies ranging from 81.8 to 86.7%. No statistical differences were detected among the five CNNs. Notably, the models trained with Group B dataset (CBCT-generated panoramic images) achieved enhanced performance as tested on Group A dataset. The AUC values reached 0.984-0.996, and the accuracies ranged between 94.5% and 98.1%. CNNs outperformed dental professionals in classification performance, and the AUC values achieved by dental specialist, novice dentist, and dental graduate student were only 0.806, 0.767 and 0.730, respectively.
CONCLUSION: CNN-based deep learning system demonstrated higher accuracy in the detection of C-shaped MSMs on panoramic radiographs compared to dental professionals. CBCT-generated panoramic images can serve as a substitute for conventional panoramic images in the training of CNN models when the quantity and quality of conventional panoramic image dataset is insufficient.
CLINICAL RELEVANCE: CNN-based deep learning models have demonstrated significant potential in assisting dentists with the identification of C-shaped MSMs on panoramic radiographs, which facilitating more effective, efficient and safer dental treatment.
MATERIALS AND METHODS: Panoramic radiographs and cone beam computed tomographic (CBCT) images were collected from a hospital in China and subsequently divided into two groups. In Group A, conventional panoramic images and CBCT images were derived from the same patients (n = 730 individuals), and the dataset consisted of conventional panoramic image patches of 1453 MSMs. In Group B (n = 610 individuals), the patients underwent CBCT examinations in the absence of available panoramic images; CBCT images were acquired and utilized to generate simulated panoramic images, and the dataset consisted of image patches of 1211 MSMs. Five pretrained CNN networks (ResNet-101 and - 50, DenseNet-121 and - 161, and Inception-V3) were utilized for the classification of C-shaped and non-C-shaped MSMs. Finally, the networks trained on the Group B dataset were tested on the Group A dataset. The diagnostic performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis, and the CBCT images were taken as the gold standard. The results were compared with those achieved by three dental professionals.
RESULTS: In Group A, all five networks exhibited satisfactory diagnostic performance, with AUC values ranging from 0.875 to 0.916 and accuracies ranging from 81.8 to 86.7%. No statistical differences were detected among the five CNNs. Notably, the models trained with Group B dataset (CBCT-generated panoramic images) achieved enhanced performance as tested on Group A dataset. The AUC values reached 0.984-0.996, and the accuracies ranged between 94.5% and 98.1%. CNNs outperformed dental professionals in classification performance, and the AUC values achieved by dental specialist, novice dentist, and dental graduate student were only 0.806, 0.767 and 0.730, respectively.
CONCLUSION: CNN-based deep learning system demonstrated higher accuracy in the detection of C-shaped MSMs on panoramic radiographs compared to dental professionals. CBCT-generated panoramic images can serve as a substitute for conventional panoramic images in the training of CNN models when the quantity and quality of conventional panoramic image dataset is insufficient.
CLINICAL RELEVANCE: CNN-based deep learning models have demonstrated significant potential in assisting dentists with the identification of C-shaped MSMs on panoramic radiographs, which facilitating more effective, efficient and safer dental treatment.