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Deep Learning for Caries Detection and Classification
Luya Lian1, Tianer Zhu1, Fudong Zhu1
1Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou 310006, China.
Deep learning models accurately detect and classify dental caries on panoramic radiographs, performing comparably to expert dentists. These AI tools show promise for improving dental diagnostics and treatment planning.
Area of Science:
- Artificial Intelligence in Dentistry
- Radiographic Interpretation
- Deep Learning for Medical Imaging
Background:
- Deep learning (DL) demonstrates significant diagnostic capabilities in radiology.
- Accurate detection and classification of dental caries are crucial for effective treatment.
- Current diagnostic methods rely heavily on expert interpretation of radiographic images.
Purpose of the Study:
- To employ DL methods for detecting caries lesions on dental panoramic films.
- To classify caries lesions based on their radiographic extension (dentin depth).
- To compare the DL model's performance against expert dentists.
Main Methods:
- 1160 dental panoramic films were analyzed, with caries lesions marked by dentists to create a reference dataset.
- A convolutional neural network (nnU-Net) was used for caries lesion detection.
- DenseNet121 was utilized for classifying lesion depth (D1, D2, D3), with performance metrics compared to six expert dentists.
Main Results:
- nnU-Net achieved high performance in caries lesion segmentation (IoU: 0.785, Dice: 0.663) and detection (accuracy: 0.986, recall: 0.821).
- DenseNet121 demonstrated strong accuracy in classifying caries depth (D1: 0.957, D2: 0.832, D3: 0.863).
- The DL models' performance metrics (accuracy, precision, recall, NPV, F1-score) were statistically similar to those of experienced dentists.
Conclusions:
- Deep learning methods exhibit comparable performance to expert dentists in detecting and classifying dental caries on panoramic radiographs.
- The study highlights the potential of AI in enhancing dental disease diagnosis.
- Further research is recommended to explore the clinical impact of these DL models on diagnostic and treatment decisions.
