Work

Radiomics-based classification of pediatric dental trauma in periapical radiographs: a preliminary study

Year: 2025

Type: article

Abstract: PURPOSE: To evaluate the feasibility and effectiveness of radiomic analysis of the periapical region on periapical radiographs for classifying the subtypes of dental trauma in pediatric patients. METHODS: A retrospective analysis was conducted on 111 pediatric patients who presented with dental trauma and underwent periapical radiography. Patients were categorized into tooth concussion (n = 23) and tooth fracture (n = 88) groups on the basis of the type of injury. Patients were randomly stratified into training (n = 78; concussion: 16, fracture: 62) and testing (n = 33; concussion: 7, fracture: 26) cohorts at a 7:3 ratio. Regions of interest were manually delineated around the apical foramen, and radiomic features were subsequently extracted. Feature selection was performed using the intraclass correlation coefficient, the Pearson correlation coefficient, and one-way ANOVA. A support vector machine classifier was constructed based on the selected features. The performance of the radiomic model was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: A total of 21 radiomic features were selected to construct the final model. In the training cohort, the model achieved an AUC of 0.766 (95% confidence interval (CI): 0.605–0.927), with a sensitivity of 1.000 and a specificity of 0.500 in differentiating the subtypes of dental trauma. In the testing cohort, the model yielded an AUC of 0.758 (95% CI: 0.538–0.979), with a sensitivity of 0.808 and a specificity of 0.714. CONCLUSION: Radiomic analysis of periapical radiographs shows promise in distinguishing between tooth concussion and fracture in pediatric patients. Further validation is needed to confirm its clinical utility and broader applicability.

Source: BMC Medical Imaging

DOI: 10.1186/s12880-025-01877-w

Authors: Mengtian Peng, Bin Yu, Juan Hu, Xiaoxin Xie, Jihong He

Institution: Deyang Stomatological Hospital

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,090