Short Observation Period and Aggressive Discharge of Patients With Head Injury and Serial CT Scans

Background: Many patients undergo two head computed tomography (CT) scans after mild traumatic brain injury (TBI). Radiographic progression without clinical deterioration does not usually alter management. Evidence-based guidelines offer potential for limited repeat imaging and safe discharge. This study characterizes patients who had two head CTs in the Emergency Department (ED), determines the change between initial and repeat CTs, and describes timing of repeat scans. Methods: This retrospective series includes all patients with head CTs during the same ED visit at an urban trauma center between May 1st, 2016 and April 30th, 2018. Radiographic interpretation was coded as positive, negative, or equivocal. Results: Of 241 subjects, the number of positive, negative, and equivocal initial CT results were 154, 50, and 37, respectively. On repeat CT, 190 (78.8%) interpretations were congruent with the original scan. Out of the 21.2% of repeat scans that diverged from the original read, 14 (5.8%) showed positive to negative conversion, 1 (.4%) showed positive to equivocal conversion, 2 (.88%) showed negative to positive conversion, 20 (8.3%) showed equivocal to negative conversion, and 14 (5.8%) showed equivocal to positive conversion. Average time between scans was 4.4 hours, and median length of stay was 10.2 hours. Conclusions: In this retrospective review, most repeat CT scans had no new findings. A small percentage converted to positive, rarely altering clinical management. This study demonstrates the need for continued prospective research to update clinical guidelines that could reduce admission and serial CT scanning for mild TBI.

Effectiveness of Problem-Based Learning versus Traditional Teaching Methods in Improving Acquisition of Radiographic Interpretation Skills among Dental Students—A Systematic Review and Meta-Analysis

Problem-based learning is an experiential and student-centred learning method to practice important skills like querying, critical thinking, and collaboration through pair and group work. The study is aimed at comparing the effectiveness of problem-based learning (PBL) and traditional teaching (TT) methods in improving acquisition of radiographic interpretation skills among dental students. Clinical trials (randomized and nonrandomized) were conducted with the help of dental students studying oral radiology using PBL and TT methods and assessing radiographic interpretation skills, knowledge scores, and satisfaction level as outcomes. Articles published from PubMed/MEDLINE, DOAJ, Cochrane Central Register of Controlled Trials, and Web of Science were searched. The quality of the studies was evaluated using the Cochrane Collaboration Tool, the MINORS Checklist, and the Risk of Bias in Nonrandomized Studies of Interventions (ROBIN-I) tool. Meta-analysis was done using Review Manager 5.3. There were twenty-four articles for qualitative synthesis and 13 for meta-analysis. The cumulative mean difference was found to be 0.54 (0.18, 0.90), 4.15 (-0.35, 8.65), and -0.14 (-0.36, 0.08) for radiographic interpretation skills, knowledge scores, and satisfaction level, respectively, showing significant difference favouring PBL as compared to TT except for satisfaction level which favoured the TT group. To understand the long-term effectiveness of PBL over TT methods in oral radiology among dental students, well-designed long-term randomized controlled trials are needed.

Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection

Tumor classification and segmentation problems have attracted interest in recent years. In contrast to the abundance of studies examining brain, lung, and liver cancers, there has been a lack of studies using deep learning to classify and segment knee bone tumors. In this study, our objective is to assist physicians in radiographic interpretation to detect and classify knee bone regions in terms of whether they are normal, begin-tumor, or malignant-tumor regions. We proposed the Seg-Unet model with global and patched-based approaches to deal with challenges involving the small size, appearance variety, and uncommon nature of bone lesions. Our model contains classification, tumor segmentation, and high-risk region segmentation branches to learn mutual benefits among the global context on the whole image and the local texture at every pixel. The patch-based model improves our performance in malignant-tumor detection. We built the knee bone tumor dataset supported by the physicians of Chonnam National University Hospital (CNUH). Experiments on the dataset demonstrate that our method achieves better performance than other methods with an accuracy of 99.05% for the classification and an average Mean IoU of 84.84% for segmentation. Our results showed a significant contribution to help the physicians in knee bone tumor detection.

Bone Quality of the Dento-Maxillofacial Complex and Osteoporosis. Opportunistic Radiographic Interpretation

Research suggests the use of different indexes on panoramic radiography as a way to assess BMD and to be able to detect changes in bone metabolism before fractures occur. Therefore, the objective of this chapter is to describe the use of these parameters as an auxiliary mechanism in the detection of low bone mineral density, as well as to characterize the radiographic findings of patients with osteoporosis.