Investigation of incidental findings in CBCT images of pediatric patients: a retrospective study
Background: Cone beam computed tomography (CBCT) allows detailed imaging of teeth and jaw structures in children. These scans may reveal findings that are unrelated to the initial reason for imaging. This raises questions about how common such findings are in pediatric patients. The aim of this study was to determine the type and frequency of incidentally detected findings in the maxillofacial region in CBCT images of patients in the pediatric and adolescent age group. Methods: Incidental findings detected in six regions, including air spaces, dental, bone, jaw lesions, temporomandibular joint (TMJ), and soft tissue calcifications, were recorded in 194 CBCT images obtained in 3 different field of view (FOV) sizes. The relationship between incidental findings and gender was examined using the chi-square test, and the relationship between age and incidental findings was examined using Spearman correlation analysis. Results: A total of 1187 incidental findings were identified in 189 (97.4%) of 194 paediatric patients on CBCT images. The most common findings were inferior concha hypertrophy (59.8%), maxillary sinus septa (58.2%), and root dilaceration (55.7%). Dental findings were observed in 89.2% of patients, and airway findings in 76.8%. Incidental findings in the bony structures and temporomandibular joint regions were significantly more prevalent among girls (p = 0.04). A positive correlation was found between age and the number of incidental findings. The number of findings was influenced by FOV size and imaging region. Conclusions: Incidental findings, especially dental and airway anomalies, are common in pediatric CBCT scans. Early detection of TMJ and airway issues is important, but routine CBCT use in children for these alone is not advised due to radiation exposure.
Publication Details
- Journal
- Journal of Clinical Pediatric Dentistry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.22514/jocpd.2025.120
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00