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An automated diagnostic support system for jaw pathologies on panoramic radiographs: a DenseNet121-CBAM deep learning
Dr Darpit Brahmbhatt1, Dr Jigna S Shah1
1Department of Oral Medicine and Radiology, Government Dental College and Hospital, Ahmedabad, 380016, India.
Objectives:
To develop and prospectively validate a novel deep learning model, integrating a DenseNet architecture with a Convolutional Block Attention Module (CBAM), for the automated classification of six common jaw pathologies from orthopantomogram (OPG) images.
Methods:
A retrospective dataset of 236 histopathologically confirmed pathological OPGs (Ameloblastoma, Dentigerous Cyst, Odontogenic Keratocyst, Radicular Cyst, Squamous Cell Carcinoma, Osteomyelitis) and 5200 normal OPGs was used for model training and pre-training. The DenseNet121-CBAM model was trained using transfer learning, data augmentation, and a weighted sampler to address class imbalance. Performance was prospectively validated on an independent cohort of 228 cases, with histopathological diagnosis serving as the ground truth.
Results:
On the prospective test set, the AI model achieved an overall accuracy of 92.54 % in classifying jaw pathologies against the histopathological gold standard. The model demonstrated robust class-specific performance, with F1-scores ranging from 90 % to 98 %.
Conclusions:
The attention-augmented deep learning model provides a rapid and highly accurate method for diagnosing common jaw pathologies on OPGs. Its performance, validated against histopathology, establishes its potential as a reliable diagnostic support tool to enhance clinical workflows and aid in early pathology detection.
