Diagnostic Accuracy Study
Radiographic assessment of post-endodontic filling features on PAN and CBCT: diagnostic agreement of an Al platform against CBCT consensus
This retrospective diagnostic accuracy study evaluated the performance of an artificial intelligence (AI) platform (Diagnocat) in assessing endodontic treatment features via panoramic (PAN) and cone-beam computed tomography (CBCT) images from 163 patients. Two experienced observers provided consensus readings on CBCT, which served as the reference standard. The AI analyzed five treatment variables. Diagnocat showed excellent diagnostic performance on CBCT (accuracy >94%, 100% sensitivity for overfilling). On PAN, performance was lower (accuracies 68.25%-84.66%), demonstrating modality-dependent performance.
This study highlights AI's potential to significantly enhance diagnostic accuracy in endodontics, particularly with CBCT imaging. The findings underscore a clear opportunity for dental practices to leverage AI for improved treatment assessment and efficiency.
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Overall CBCT Accuracy (%)
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Overfilling Sensitivity on CBCT (%)
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Min. PAN Accuracy (%)
Deep Analysis & Enterprise Applications
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94.71%
CBCT Diagnostic Accuracy (Adequate Obturation)
The AI platform demonstrated exceptional diagnostic accuracy for adequate obturation when analyzing CBCT images, exceeding 94% across all parameters.
Enterprise Process Flow
The AI platform processes images through a structured pipeline for diagnosis.
CBCT Image Upload
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AI Analysis (Modality-Specific Pipeline)
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Feature Detection (5 Variables)
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Probability Output (0-100%)
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Binary Classification (>50% positive)
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Diagnostic Report Generation
| Metric | CBCT Performance | PAN Performance |
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| Overall Accuracy |
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| Overfilling Sensitivity |
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| Adequate Density Precision |
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A comparative overview highlights the superior diagnostic capabilities of AI when applied to CBCT images compared to PAN.
AI's Modality-Dependent Diagnostic Strength
The study revealed that the AI platform's diagnostic performance is strongly dependent on the imaging modality. While highly accurate for CBCT, its performance on PAN images showed notable limitations, especially concerning 'adequate density' and 'adequate obturation'. This underscores the need for contextual interpretation of AI outputs based on the imaging source.
- CBCT-based AI achieved >94% accuracy.
- PAN-based AI showed lower accuracy (68-84%).
- Lowest reliability for 'adequate density' on both modalities.
- Performance comparable to human readers on PAN but with reduced sensitivity.
71.43%
AI PAN Accuracy (Adequate Density)
The relatively lower accuracy for adequate density on PAN images suggests that AI, like human readers, struggles with the inherent limitations of 2D radiography for this specific feature.
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Your AI Implementation Roadmap
A typical journey to integrate advanced AI diagnostics into your enterprise, ensuring a smooth transition and maximum impact.
Data Integration & Pre-processing
Securely integrate existing dental imaging archives (PAN, CBCT) into the AI platform, ensuring data privacy and quality. Standardize formats and metadata for optimal AI ingestion.
Model Customization & Training
Fine-tune AI models to specific institutional protocols and demographic variations using a subset of de-identified patient data, enhancing diagnostic accuracy for unique case mixes.
Pilot Deployment & Validation
Deploy the AI tool in a controlled pilot environment, validating its performance against established clinical consensus and gathering feedback from dental professionals on usability and accuracy.
Full-Scale Integration & Monitoring
Integrate the AI platform into routine clinical workflows, providing continuous monitoring and iterative improvements based on real-world diagnostic outcomes and practitioner insights.
Performance Optimization & Scaling
Regularly update AI algorithms with new data and research findings. Scale deployment across multiple departments or practices, ensuring sustained high performance and cost-effectiveness.
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