AIMC Topic: Models, Dental

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Evaluating masked self-supervised learning frameworks for 3D dental model segmentation tasks.

Scientific reports May 14, 2025
The application of deep learning using dental models is crucial for automated computer-aided treatment planning. However, developing highly accurate models requires a substantial amount of accurately labeled data. Obtaining this data is challenging, ...
Humans Deep Learning Imaging, Three-Dimensional Supervised Machine Learning Tooth Models, Dental
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Development and validation of a graph convolutional network (GCN)-based automatic superimposition method for maxillary digital dental models (MDMs).

The Angle orthodontist May 1, 2025
OBJECTIVES: To validate the accuracy and reliability of a graph convolutional network (GCN)-based superimposition method of a maxillary digital dental model (MDM) by comparing it with manual superimposition and quantifying the clinical error from thi...
Humans Neural Networks, Computer Reproducibility of Results Imaging, Three-Dimensional Image Processing, Computer-Assisted Maxilla Models, Dental
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A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Tooth segmentation from intraoral scans is a crucial part of digital dentistry. Many Deep Learning based tooth segmentation algorithms have been developed for this task. In most of the cases, high accuracy has been achieved, although, most of the ava...
Algorithms Deep Learning Radionuclide Imaging Tooth Models, Dental
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