Automatically Diagnosing Skull Fractures Using an Object Detection Method and Deep Learning Algorithm in Plain Radiography Images

A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging

Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.

A Systematic Review and Meta-Analysis on the Management and Outcome of Isolated Skull Fractures in Pediatric Patients

BACKGROUND

The impact of traumatic brain injury (TBI) on the pediatric population is profound. The aim of this study is to unveil the state of the evidence concerning acute neurosurgical intervention, hospitalizations after injury, and neuroimaging in isolated skull fractures (ISF).

MATERIALS AND METHODS

This systematic review was conducted in accordance with PRISMA guidelines. PubMed, Cochrane, Web of Science, and Embase were searched for papers until April 2023. Only ISF cases diagnosed via computed tomography were considered.

RESULTS

A total of 10,350 skull fractures from 25 studies were included, of which 7228 were ISF. For the need of acute neurosurgical intervention, the meta-analysis showed a risk of 0% (95% CI: 0-0%). For hospitalization after injury the calculated risk was 78% (95% CI: 66-89%). Finally, for the requirement of repeated neuroimaging the analysis revealed a rate of 7% (95% CI: 0-15%). No deaths were reported in any of the 25 studies.

CONCLUSIONS

Out of 7228 children with ISF, an almost negligible number required immediate neurosurgical interventions, yet a significant 74% were hospitalized for up to 72 h. Notably, the mortality was zero, and repeat neuroimaging was uncommon. This research is crucial in shedding light on the outcomes and implications of pediatric TBIs concerning ISFs.