International Journal of Oral and Maxillofacial Surgery
Artifical IntelligenceAssessment of deep convolutional neural network models for mandibular fracture detection in panoramic radiographs
Section snippets
Dataset acquisition
A radiology report database was queried retrospectively to obtain panoramic radiographs of patients aged ≥18 years treated in the oral and maxillofacial clinic of a regional trauma centre between 2016 and 2020. A total of 1710 radiographic images were retrieved. These were divided into 855 images containing mandibular fractures (1423 fracture sites across six anatomical regions of the mandible, as shown in Table 1) and 855 images without fractures. The panoramic radiographs containing a
Performance of the binary classification models
The binary classification performance of the models for the presence of a fracture when evaluated on the overall hold-out test set is reported in Table 2. The image classification of DenseNet-169 achieved a precision of 100%, recall of 100%, F1 score of 100%, sensitivity of 100%, and specificity of 100%. The image classification of ResNet-50 achieved a precision of 99%, recall of 100%, F1 score of 100%, sensitivity of 100%, and specificity of 99%. In addition, the binary classification ROC
Discussion
Misdiagnosis is the primary cause of malpractice. There are multiple factors that can contribute to radiographic misinterpretation of fractures by clinicians, including clinician fatigue, lack of specialized expertise, and inconsistency among reading clinicians.6, 7 Misinterpretation of mandibular fractures in radiographs may have grave consequences, resulting in complications including infection, non-union, or malunion, which may lead to problems with masticatory functions and facial
Funding
This study was supported by a Thammasat University Research Grant (TUFT 20/2565).
Ethical approval
Approval was obtained from Thammasat University Research Ethics Committee (COA 114/2564).
Patient consent
Not required.
Competing interests
None.
Acknowledgements
The authors gratefully acknowledge Dr Kan Chantaraniyom, Dr Nantaporn Nualjanthuek, and Dr Wanliaka Kesorn from the Oral and Maxillofacial Clinic, Saraburi Hospital. The support of a Thammasat University Research Grant (TUFT2565) and the provision of the TitanXP GPU used in this research by Nvidia Corporation are also gratefully acknowledged. The authors thank Waranthorn Chansawang for their assistance with the deep learning model training.
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