Int J Stomatol

Int J Stomatol ›› 2026, Vol. 53 ›› Issue (3): 328-334.doi: 10.7518/gjkq.2026218

• Digitization • Previous Articles    

Application of convolutional neural networks ResNet-101 and Inception-v3 for detecting three-rooted mandibular first molars in panoramic radiographs

Wenyuan Zhou1(),Juan Fan1,Zaidao Xiong1,Lin Zhu1,Zezheng Yu1,Lu Wang1,Long Jin2,Panpan Zhang3,Yongchun Gu1()   

  1. 1.Dept. of Dentistry and Central Lab, Ninth People’s Hospital of Suzhou, Soochow University, Suzhou 215200, China
    2.Dept. of Radiology, Ninth People’s Hospital of Suzhou, Soochow University, Suzhou 215200, China
    3.Dept. of Otorhinolaryngology, Ninth People’s Hospital of Suzhou, Soochow University, Suzhou 215200, China
  • Received:2024-12-04 Revised:2025-10-30 Online:2026-05-01 Published:2026-04-24
  • Contact: Yongchun Gu E-mail:zhouwenyuan_zwy@163.com;guyc7152@163.com
  • Supported by:
    Medical Health Science and Technology Innovation Project(SKY2022030)

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Abstract:

Objective This study aimed to develop a convolutional neural network (CNN)-based deep learning system for detecting three-rooted mandibular first molars (MFMs) on panoramic radiographs and evaluate its diagnostic performance. Methods Cone beam computed tomography (CBCT) and panoramic radiographs were obtained from the Department of Oral Radiology. The patients were assigned into two groups. In group A, CBCT and conventional panoramic images were derived from the same patients, and the dataset comprised image patches of 1 444 MFMs (of which, 367 teeth had three roots). In group B, 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 1 203 MFMs (of which, 283 teeth had three roots). Two CNNs (ResNet-101 and Inception-v3) were employed to classify three- and two-rooted MFMs based on training and testing with two groups of image patches. Receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance, using the CBCT examination as the gold standard. The performance of CNNs was compared with that of five dental professionals. Results Both CNN models achieved satisfactory performance in their respective evaluations. In Group A, the accuracy, sensitivity, and specificity of ResNet-101 were 87.5%, 83.6%, and 88.9%, respectively, and the AUC value (0.908) was significantly higher than 0.857 achieved by Inception-v3 (P<0.01). Notably, the CNN models trained on CBCT-generated panoramic images (group B dataset) achieved comparable performance when tested on conventional panoramic images (group A dataset). For ResNet-101, the accuracy, sensitivity, specificity, and AUC were 85.1%, 75.8%, 88.1%, and 0.893, respectively. The five dental professionals achieved significantly lower diagnostic performance compared with CNNs, and the AUC values were 0.532-0.668. Conclusion The CNN-based deep learning system exhibited a high level of accuracy in detecting three-rooted MFMs on panoramic radiographs. CBCT-generated panoramic images can effectively replace conventional panoramic images in the training of CNN models when the quantity and quality of conventional images are inadequate.

Key words: deep learning, convolutional neural network, panoramic radiography, cone beam computed tomography, mandibular first molar

CLC Number: 

  • R816.98

TrendMD: 

Fig 1

Extraction of MFM image patches"

Tab 1

Diagnostic performance of two CNN models and five clinicians"

CNN模型准确率AUC95%渐近置信区间灵敏度特异性阳性预测值阴性预测值F1值
下限上限
A组（常规口腔曲面体层片：1 444 MFM）
ResNet-1010.8750.9080.8690.9450.8360.8890.7190.9410.773
Inception-v30.8380.8570.8180.9050.7450.870.6610.9090.701
高年资医师0.7810.6680.6030.7280.4090.9070.6000.8180.485
低年资医师10.7550.6640.6170.7080.4820.8450.5150.8270.500
低年资医师20.7300.6570.6100.7020.5090.8050.4710.8280.489
硕士研究生10.7300.5410.4920.5880.2460.8360.3370.7650.283
硕士研究生20.5610.5320.4650.5970.4720.5910.3430.7120.348
B组（CBCT生成的模拟口腔曲面体层片：1 203 MFM）
ResNet-1010.9360.9240.8790.9630.7930.9780.9150.9410.850
Inception-v30.8770.8870.8400.9300.6830.9350.7570.9080.718
B组全部图像为训练集，A组全部图像为测试集
ResNet-1010.8510.8930.8720.9130.7580.8810.6780.9170.716
Inception-v30.7620.8680.8410.8870.8500.7330.5120.9370.639

Fig 2

ROC curve analysis of two CNN models and five clinicians in the classification of image patches of MFMs"

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