AI Article Synopsis
- The study aims to improve the detection of impacted teeth using a refined version of the MedSAM model, trained on 1,016 X-ray images.
- The dataset was divided into training, validation, and testing sets, with the model focusing on tooth centers to enhance segmentation accuracy.
- Results showed the model achieving an accuracy of 86.73% and highlighted the need for further enhancements to improve diagnostic capabilities for dental practitioners.
Article Abstract
Objective: The aim is to detect impacted teeth in panoramic radiology by refining the pretrained MedSAM model.
Study Design: Impacted teeth are dental issues that can cause complications and are diagnosed via radiographs. We modified SAM model for individual tooth segmentation using 1016 X-ray images. The dataset was split into training, validation, and testing sets, with a ratio of 16:3:1. We enhanced the SAM model to automatically detect impacted teeth by focusing on the tooth's center for more accurate results.
Results: With 200 epochs, batch size equals to 1, and a learning rate of 0.001, random images trained the model. Results on the test set showcased performance up to an accuracy of 86.73%, F1-score of 0.5350, and IoU of 0.3652 on SAM-related models.
Conclusion: This study fine-tunes MedSAM for impacted tooth segmentation in X-ray images, aiding dental diagnoses. Further improvements on model accuracy and selection are essential for enhancing dental practitioners' diagnostic capabilities.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11456186 | PMC |
| http://dx.doi.org/10.1177/11795972241288319 | DOI Listing |
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