• Journal of Korean Neurosurgical Society
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• 제52권6호
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• pp.528-533
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• 2012

Objective : In the pediatric population the skull has not yet undergone ossification and it is assumed that the diagnostic rate of skull fractures by simple X-rays are lower than that of adults. It has been recently proposed that the diagnostic rates of skull fractures by 3-dimensional computer tomography (3D-CT) are higher than simple X-rays. The authors therefore attempted to compare the diagnostic rates of pediatric skull fractures by simple X-rays and 3D-CTs with respect to the type of fracture. Methods : One-hundred patients aged less than 12 years who visited the Emergency Center for cranial injury were subject to simple X-rays and 3D-CTs. The type and location of the fractures were compared and Kappa statistical analysis and the t-test were conducted. Results : Among the 100 pediatric patients, 65 were male and 35 were female. The mean age was $50{\pm}45$ months. 63 patients had simple skull fractures and 22 had complex fractures, and the types of fractures were linear fractures in 74, diastatic fractures 15, depressed fractures in 10, penetrating fracture in 1, and greenstick fractures in 3 patients. Statistical difference was observed for the predictive value of simple skull fractures' diagnostic rate depending on the method for diagnosis. A significant difference of the Kappa value was noted in the diagnosis of depressed skull fractures and diastatic skull fractures. Conclusion : In the majority of pediatric skull fractures, 3D-CT showed superior diagnosis rates compared to simple skull X-rays and therefore 3D-CT is recommended whenever skull fractures are suspected. This is especially true for depressed skull fractures and diastatic skull fractures.

• Journal of Korean Neurosurgical Society
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• 제66권1호
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• pp.53-62
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• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.

• Journal of Yeungnam Medical Science
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• 제7권2호
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• pp.173-179
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• 1990

외상성 뇌경막의 혈종의 비수술적 치료에 대한 기준은 보고자에 따라 상이하고 비수술적 요법시 환자를 집중관찰해야 되고, 입원기간이 길며 여러번 CT 촬영을 해야 하는 위험성이 있으나 상시정맥동 주위의 경미한 뇌경막외 혈종의 경우 시도해 볼 수 있는 한 방법으로 판단된다. 저자들은 다른 부위의 뇌경막외 혈종과는 달리 임상증상 및 국소증후가 적고 단순 뇌압상승증세가 주 증상인 상시정맥동 주위의 뇌경막외 혈종 환자에서 집중적인 관찰과 추적 및 고식요법으로 좋은 결과를 얻었기에 비수술적 치료의 대상, 방법등에 관하여 문헌고찰과 함께 보고하는 바이다.