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Topology of a Smile: Persistent Homology in Dental Imaging
Not provided in the abstract
topological data analysisdental imagingautomationpersistent homology
2608.21422
Builder Relevance
2h ago80%
Abstract
The paper proposes an automated approach using persistent homology for classifying teeth in CBCT scans, achieving high accuracy in diagnostics.
Reality Card
Core Claim
The proposed method achieves an average accuracy of 97.67% for tooth-labeling and 96.77% for diagnostic tasks, significantly outperforming traditional CNN approaches.
Method / Result
Achieved average accuracy scores of 97.67% for tooth-labeling and 96.77% for diagnostics.
Limitations
The abstract does not specify limitations or concerns regarding reproducibility.
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