Deep Learning Model for Predicting Endodontic Treatment Outcomes
Author Information
Author(s): Dennis, Suebnukarn Siriwan, Vicharueang Sothana, Limprasert Wasit
Primary Institution: Thammasat University
Hypothesis
The integration of the Mask R-CNN model with clinicians would improve the accuracy of predicting endodontic treatment outcomes on preoperative periapical radiographs compared to predictions made by clinicians alone.
Conclusion
The deep learning-based Mask R-CNN model demonstrated high performance in classifying endodontic treatment outcomes and improved clinician accuracy when used as a decision-support tool.
Supporting Evidence
- The Mask R-CNN model achieved a mean average precision (mAP) of 0.88.
- Clinician performance improved significantly with the help of the Mask R-CNN model.
- The model's predictions were evaluated against a test set of 120 images.
Takeaway
This study created a smart computer program that helps dentists predict how well root canal treatments will work by looking at X-ray images of teeth.
Methodology
The study used a retrospective design to develop and evaluate a deep learning model based on preoperative periapical radiographic images, employing a Mask R-CNN segmentation algorithm.
Potential Biases
Potential biases may arise from the retrospective nature of the study and the limited dataset from a single institution.
Limitations
The study was limited to retrospective data from a single hospital and did not include other important preoperative patient history.
Participant Demographics
The sample included 1200 cases, with 46.3% male and 53.7% female, primarily aged 25-64 years.
Statistical Information
Confidence Interval
95% CI 0.83–0.93
Digital Object Identifier (DOI)
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