Automated chart filing on panoramic radiographs using deep learning
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Published on: February 23, 2024
Shankeeth Vinayahalingam1, Ru-Shan Goey2, Steven Kempers3
1Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands; Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands; Department of Oral and Maxillofacial Surgery, Universitätsklinikum Münster, Münster, Germany.
Objective:
The aim of this study is to automatically detect, segment and label teeth, crowns, fillings, root canal fillings, implants and root remnants on panoramic radiographs (PR(s)).
Material And Methods:
As a reference, 2000 PR(s) were manually annotated and labeled. A deep-learning approach based on mask R-CNN with Resnet-50 in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1800 PR(s). Subsquently, the trained algorithm was applied onto a test set consisting of 200 PR(s). F1 scores, as a measure of accuracy, were calculated to quantify the degree of similarity between the annotated ground-truth and the model predictions. The F1-score considers the harmonic mean of precison (positive predictive value) and recall (specificity).
Results:
The proposes method achieved F1 scores up to 0.993, 0.952 and 0.97 for detection, segmentation and labeling, respectivley.
Conclusion:
The proposed method forms a promising foundation for the further development of automatic chart filing on PR(s).
Clinical Significance:
Deep learning may assist clinicians in summarizing the radiological findings on panoramic radiographs. The impact of using such models in clinical practice should be explored.
