A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes
Abstract Background Orthopantomograms (OPGs) are essential diagnostic tools in dental and maxillofacial care, providing a panoramic view of the jaws, teeth, and surrounding bone structures. Detecting bone loss, which indicates periodontal disease and systemic conditions like osteoporosis, is crucial...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | Head & Face Medicine |
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| Online Access: | https://doi.org/10.1186/s13005-025-00528-3 |
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| author | Pradeep Kumar Yadalam Amit Rajabhau Pawar Prabhu Manickam Natarajan Carlos M. Ardila |
| author_facet | Pradeep Kumar Yadalam Amit Rajabhau Pawar Prabhu Manickam Natarajan Carlos M. Ardila |
| author_sort | Pradeep Kumar Yadalam |
| collection | DOAJ |
| description | Abstract Background Orthopantomograms (OPGs) are essential diagnostic tools in dental and maxillofacial care, providing a panoramic view of the jaws, teeth, and surrounding bone structures. Detecting bone loss, which indicates periodontal disease and systemic conditions like osteoporosis, is crucial for early diagnosis and treatment planning. Periodontists use OPGs to identify subtle radiographic features that signify different stages of bone loss. Automated systems integrating radiographic imaging with textual notes can enhance diagnostic accuracy and minimize interobserver variability. Radiographic notes, which summarize clinical observations and preliminary interpretations, can be utilized for classification through natural language processing techniques, including Transformer-based models. This study will classify bone loss severity (normal, mild, or severe) from OPG notes using a novel dual-embedding few-shot learning framework. Methods This study used a dataset of radiographic notes from OPGs gathered at Saveetha Dental College and Hospital in Chennai. Bone loss was classified according to Glickman’s Classification system. The proposed DualFit model architecture consists of two main branches: a Text Processing Branch for converting textual data into dense vectors and a Feature Processing Branch for analyzing numerical and categorical data. Key techniques such as batch normalization and dropout layers were implemented to improve learning stability and reduce overfitting. A Fusion Layer was utilized to merge outputs from both branches, optimizing classification performance. Results The DualFit model outperformed leading models like BioBERT, ClinicalBERT, and PubMedBERT. It attained an accuracy of 98.98%, precision of 98.71%, recall of 99.14%, and an F1-score of 98.92%, marking a 5.53% accuracy increase over PubMedBERT. Additionally, the model excelled in multi-class classification tasks, ensuring class balance and achieving near-perfect values for precision, recall, and area under both the ROC and precision-recall curves. Conclusions The DualFit model significantly advances the automated classification of OPG radiographic notes related to periodontal bone loss. Outperforming existing Transformer-based models streamlines the diagnostic workflow, reduces the workload of radiologists, and enables timely interventions for improved patient outcomes. Future work should explore external validation and integration with multimodal diagnostic systems. |
| format | Article |
| id | doaj-art-667bb21dce56452da67ef9391c0fdd0a |
| institution | Kabale University |
| issn | 1746-160X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Head & Face Medicine |
| spelling | doaj-art-667bb21dce56452da67ef9391c0fdd0a2025-08-20T03:46:28ZengBMCHead & Face Medicine1746-160X2025-07-0121111310.1186/s13005-025-00528-3A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notesPradeep Kumar Yadalam0Amit Rajabhau Pawar1Prabhu Manickam Natarajan2Carlos M. Ardila3Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS)Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS)Department of Clinical Sciences, Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry, Ajman UniversityDepartment of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS)Abstract Background Orthopantomograms (OPGs) are essential diagnostic tools in dental and maxillofacial care, providing a panoramic view of the jaws, teeth, and surrounding bone structures. Detecting bone loss, which indicates periodontal disease and systemic conditions like osteoporosis, is crucial for early diagnosis and treatment planning. Periodontists use OPGs to identify subtle radiographic features that signify different stages of bone loss. Automated systems integrating radiographic imaging with textual notes can enhance diagnostic accuracy and minimize interobserver variability. Radiographic notes, which summarize clinical observations and preliminary interpretations, can be utilized for classification through natural language processing techniques, including Transformer-based models. This study will classify bone loss severity (normal, mild, or severe) from OPG notes using a novel dual-embedding few-shot learning framework. Methods This study used a dataset of radiographic notes from OPGs gathered at Saveetha Dental College and Hospital in Chennai. Bone loss was classified according to Glickman’s Classification system. The proposed DualFit model architecture consists of two main branches: a Text Processing Branch for converting textual data into dense vectors and a Feature Processing Branch for analyzing numerical and categorical data. Key techniques such as batch normalization and dropout layers were implemented to improve learning stability and reduce overfitting. A Fusion Layer was utilized to merge outputs from both branches, optimizing classification performance. Results The DualFit model outperformed leading models like BioBERT, ClinicalBERT, and PubMedBERT. It attained an accuracy of 98.98%, precision of 98.71%, recall of 99.14%, and an F1-score of 98.92%, marking a 5.53% accuracy increase over PubMedBERT. Additionally, the model excelled in multi-class classification tasks, ensuring class balance and achieving near-perfect values for precision, recall, and area under both the ROC and precision-recall curves. Conclusions The DualFit model significantly advances the automated classification of OPG radiographic notes related to periodontal bone loss. Outperforming existing Transformer-based models streamlines the diagnostic workflow, reduces the workload of radiologists, and enables timely interventions for improved patient outcomes. Future work should explore external validation and integration with multimodal diagnostic systems.https://doi.org/10.1186/s13005-025-00528-3OrthopantomogramsBone lossPeriodontitisClinicalBERTBERTText classification |
| spellingShingle | Pradeep Kumar Yadalam Amit Rajabhau Pawar Prabhu Manickam Natarajan Carlos M. Ardila A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes Head & Face Medicine Orthopantomograms Bone loss Periodontitis ClinicalBERT BERT Text classification |
| title | A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes |
| title_full | A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes |
| title_fullStr | A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes |
| title_full_unstemmed | A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes |
| title_short | A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes |
| title_sort | novel dual embedding few shot learning approach for classifying bone loss using orthopantomogram radiographic notes |
| topic | Orthopantomograms Bone loss Periodontitis ClinicalBERT BERT Text classification |
| url | https://doi.org/10.1186/s13005-025-00528-3 |
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