โก Quick Summary
This meta-analysis evaluated the diagnostic performance of artificial intelligence (AI) in identifying periodontitis through various radiographic imaging modalities. The findings revealed that panoramic imaging is particularly effective for screening and staging, while periapical radiographs excel in early detection.
๐ Key Details
- ๐ Imaging Modalities: Periapical, panoramic, bitewing, and cone-beam computed tomography (CBCT)
- โ๏ธ Classification Types: Binary classification vs. staging classification
- ๐ Performance Metrics: Sensitivity, specificity, accuracy, F1-score, area under the curve (AUC)
- ๐ Study Design: Systematic meta-analysis of AI-based periodontal diagnostic studies
๐ Key Takeaways
- ๐ Binary classification using periapical imaging achieved a sensitivity of 87.2% and specificity of 81.5%.
- ๐ Panoramic radiographs demonstrated an accuracy of 88.2% in binary classification.
- ๐ Staging classification with panoramic images yielded the highest accuracy of 88.9% and specificity of 85.4%.
- ๐ Periapical images showed higher sensitivity in staging classification at 76.4%.
- โ๏ธ Diagnostic accuracy varied significantly across imaging modalities, indicating the need for modality-specific approaches.
- ๐ก Insights from this study are crucial for the clinical integration of AI in periodontal diagnostics.
๐ Background
Periodontitis is a prevalent dental disease that can lead to tooth loss and other serious health issues if not diagnosed and treated promptly. Traditional diagnostic methods often rely on clinical examination and radiographic evaluation, which can be subjective. The advent of artificial intelligence offers a promising avenue for enhancing diagnostic accuracy and efficiency in identifying periodontal disease through radiographs.
๐๏ธ Study
This meta-analysis systematically reviewed studies that utilized AI for diagnosing periodontitis across various imaging modalities, including periapical, panoramic, bitewing, and cone-beam computed tomography (CBCT) radiographs. The researchers employed random-effects models to calculate pooled sensitivity, specificity, accuracy, F1-score, and area under the curve (AUC), providing a comprehensive overview of AI’s diagnostic capabilities.
๐ Results
The results indicated that in binary classification, periapical imaging achieved a sensitivity of 87.2% and specificity of 81.5%. Panoramic radiographs, on the other hand, demonstrated an accuracy of 88.2%. In terms of staging classification, panoramic images excelled with an accuracy of 88.9% and specificity of 85.4%, while periapical images showed a sensitivity of 76.4%. These findings highlight significant variability in diagnostic performance across different imaging modalities.
๐ Impact and Implications
The implications of this study are profound for the field of dentistry. By emphasizing modality-specific approaches, clinicians can better utilize AI technologies for diagnosing periodontitis. Panoramic imaging is recommended for screening and staging, while periapical radiographs are more suited for early detection. This tailored approach can enhance patient outcomes and streamline the diagnostic process in periodontal care.
๐ฎ Conclusion
This meta-analysis underscores the potential of artificial intelligence in revolutionizing the diagnosis of periodontitis. By leveraging the strengths of different imaging modalities, healthcare professionals can achieve more accurate and timely diagnoses. As AI continues to evolve, its integration into clinical practice promises to improve the quality of dental care significantly. Further research is encouraged to explore the full capabilities of AI in periodontal diagnostics.
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Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.
Abstract
BACKGROUND: Artificial intelligence (AI) has shown promise for diagnosing periodontal disease from dental radiographs. However, diagnostic performance across classification types (binary classification vs. staging classification) and imaging modalities remains unclear. This meta-analysis evaluates the accuracy of AI diagnostics for periodontitis, comparing binary and staging classifications across various imaging modalities.
METHODS: A systematic meta-analysis reviewed AI-based periodontal diagnostic studies using periapical, panoramic, bitewing, or cone-beam computed tomographic radiographs. Random-effects models calculated pooled sensitivity, specificity, accuracy, F1-score, and area under the curve. Subgroup analyses were performed by imaging modality and heterogeneity (Iยฒ).
RESULTS: In binary classification, periapical imaging showed a sensitivity of 87.2% and a specificity of 81.5%, while panoramic radiographs had an accuracy of 88.2%. In staging classification, panoramic images achieved the highest accuracy (88.9%) and specificity (85.4%), whereas periapical images showed higher sensitivity (76.4%). Diagnostic accuracy varied significantly across imaging modalities, contributing to heterogeneity among studies.
CONCLUSIONS: This first meta-analysis comparing binary and staging AI classification emphasizes modality-specific approaches: panoramic imaging is suitable for screening and staging, whereas periapical radiographs support early detection, providing essential insights for clinical AI integration.
Author: [‘Lee JE’, ‘Choi E’, ‘Park JB’]
Journal: BMC Oral Health
Citation: Lee JE, et al. Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities. Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities. 2025; (unknown volume):(unknown pages). doi: 10.1186/s12903-025-07171-z