We have located links that may give you full text access.
DMAF-Net: Deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs.
Dento Maxillo Facial Radiology 2024 March 23
OBJECTIVES: Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the YOLO network and verified the feasibility of this new method in aiding the detection of dental problems.
METHODS: We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect five types of dental situations (impacted teeth, missing teeth, implants, crown restorations and root canal-treated teeth) in panoramic radiography by improving the You Only Look Once (YOLO) network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptive spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set, and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria.
RESULTS: 1474 panoramic radiographs were divided into training, validation and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP [0.5:0.95]) are 91.8% and 63.7%, respectively.
CONCLUSIONS: The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching and clinical applications in the future.
METHODS: We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect five types of dental situations (impacted teeth, missing teeth, implants, crown restorations and root canal-treated teeth) in panoramic radiography by improving the You Only Look Once (YOLO) network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptive spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set, and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria.
RESULTS: 1474 panoramic radiographs were divided into training, validation and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP [0.5:0.95]) are 91.8% and 63.7%, respectively.
CONCLUSIONS: The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching and clinical applications in the future.
Full text links
Related Resources
Trending Papers
A Guide to the Use of Vasopressors and Inotropes for Patients in Shock.Journal of Intensive Care Medicine 2024 April 14
Prevention and treatment of ischaemic and haemorrhagic stroke in people with diabetes mellitus: a focus on glucose control and comorbidities.Diabetologia 2024 April 17
British Society for Rheumatology guideline on management of adult and juvenile onset Sjögren disease.Rheumatology 2024 April 17
Diagnosis and Management of Cardiac Sarcoidosis: A Scientific Statement From the American Heart Association.Circulation 2024 April 19
Albumin: a comprehensive review and practical guideline for clinical use.European Journal of Clinical Pharmacology 2024 April 13
Eosinophilic Esophagitis: Clinical Pearls for Primary Care Providers and Gastroenterologists.Mayo Clinic Proceedings 2024 April
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app