Journal of stomatology, oral and maxillofacial surgery · 2025
An automated diagnostic support system for jaw pathologies on panoramic radiographs: a DenseNet121-CBAM deep learning study with histopathological correlation.
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Brahmbhatt Darpit K, Shah Jigna S
Аннотация
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.
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.