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Health Sciences › Dentistry › Oral Surgery
A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images
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Overview
Paper Summary
Conflicts of Interest
Identified Weaknesses
Rating Explanation
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Paper Summary
Paperzilla title
AI Dentists: Almost Ready to Replace Your Dentist (But Not Quite Yet)
This study developed an AI system for automatic segmentation of teeth and alveolar bone from CBCT images. The system achieved high accuracy comparable to expert radiologists, albeit with slightly lower performance in cases with metal implants, and significantly improved efficiency. The clinical utility is demonstrated by reducing manual annotation time by approximately 97% with AI assistance.
Possible Conflicts of Interest
None identified
Identified Weaknesses
Limited external validation
While the dataset used for training and validation is large, the external testing dataset is smaller and the generalizability to diverse datasets is limited.
Lack of interpretability
The study heavily relies on deep learning, which can be a black box and may not be easily interpretable for clinical decision-making.
Computational cost analysis
The paper does not provide any analysis of the computational resources required for training and running the AI system, which may impact its practical applicability in clinical settings.
Not fully automatic
The study claims full automation, but expert review and occasional corrections were still needed, implying that the system is not truly fully automatic.
Unfair comparison
The comparison with other deep-learning methods is not entirely fair as those methods were retrained on different datasets than their original publications. Also, existing state-of-the-art methods for ROI generation and localization could have been compared but were excluded.
Reduced performance with metal implants
The Al system performs slightly worse on cases with metal implants, which is a common occurrence in dental practice, raising concerns about its robustness in real-world scenarios.
Rating Explanation
This study proposes a novel AI system for fully automatic tooth and alveolar bone segmentation from CBCT images, demonstrating promising results on a large-scale multi-center dataset. However, it still exhibits reduced performance on challenging cases and requires occasional human intervention, therefore rating capped at 4.
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Topic Hierarchy
File Information
Original Title:
A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images
File Name:
s41467-022-29637-2.pdf
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File Size:
5.04 MB
Uploaded:
July 14, 2025 at 10:50 AM
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