Sandifer syndrome is a rare manifestation of gastroesophageal reflux in children, occurring in association with abnormal movement of the head and neck and recurrent episodes of seizure after feeding. This syndrome may be misdiagnosed as infantile seizure and musculoskeletal disorder and may be associated with failure to thrive, anemia, aspiration pneumonia, and esophagitis. In this paper, Sandifer syndrome in a four months old male with recurrent aspiration pneumonia, apnea, and seizure after feeding is presented.
Anemia ; Apnea ; Child ; Dyskinesias ; Esophagitis ; Failure to Thrive ; Gastroesophageal Reflux ; Head ; Humans ; Male ; Neck ; Pneumonia, Aspiration ; Seizures

PURPOSE: This study was performed to know and compare the diagnostic usefulness of Magnetic Resonance Imaging(MRI) and Computed Tomography(CT) for the evaluation of patients with seizure. METHODS: We studied retrospectively 96 children who visited Hanyang University Hospital due to seizure and took a brain CT between January 1996 and June 1999, and 60 children who visited Hanyang University Hospital due to seizure and took a brain MRI between June 1999 and March 2002. All children who were enrolled in this study divided into few groups according to seizure type and EEG findings. RESULTS: Among 96 children who took a brain CT, 12 patients had brain abnormalities on CT, and among 60 children took a brain MRI, 13 patients had brain abnormalities on MRI. Abnormalities on CT were more frequently seen in children with focal seizure and/or focal abnormalities on EEG, but contrarily in MRI, abnormalities were more frequently seen in children with generalized seizure. CONCLUSION: Even the MRI is more expensive to be taken than the CT, MRI isrecommended when there is a need to evaluate the causes of seizures even for thepatients with generalized seizure as a result of our study.
Brain* ; Child* ; Electroencephalography ; Humans ; Magnetic Resonance Imaging* ; Retrospective Studies ; Seizures*

PURPOSE: This study was performed to know and compare the diagnostic usefulness of Magnetic Resonance Imaging(MRI) and Computed Tomography(CT) for the evaluation of patients with seizure. METHODS: We studied retrospectively 96 children who visited Hanyang University Hospital due to seizure and took a brain CT between January 1996 and June 1999, and 60 children who visited Hanyang University Hospital due to seizure and took a brain MRI between June 1999 and March 2002. All children who were enrolled in this study divided into few groups according to seizure type and EEG findings. RESULTS: Among 96 children who took a brain CT, 12 patients had brain abnormalities on CT, and among 60 children took a brain MRI, 13 patients had brain abnormalities on MRI. Abnormalities on CT were more frequently seen in children with focal seizure and/or focal abnormalities on EEG, but contrarily in MRI, abnormalities were more frequently seen in children with generalized seizure. CONCLUSION: Even the MRI is more expensive to be taken than the CT, MRI isrecommended when there is a need to evaluate the causes of seizures even for thepatients with generalized seizure as a result of our study.
Brain* ; Child* ; Electroencephalography ; Humans ; Magnetic Resonance Imaging* ; Retrospective Studies ; Seizures*

PURPOSE: Respiratory syncytial virus(RSV) mainly brings about respiratory complication, but it can also be related to neurologic complication. We have studied clinical aspect and incidence of neurologic complication associated with RSV infection. METHODS: We reviewed the medical records of 732 children with RSV infection who were admitted to the department of pediatrics, Hanyang University hospital from November 2002 to June 2007 and studied on existence of neurologic complication, age, sex, clinical symptom, characteristics of seizure, and laboratory findings. RESULTS: Neurologic complications were occurred in 52 patients(7.1%) among 732 patients. Among them, 14 patients(1.9%) had seizure and 38 patients(5.2%) had alteration of mental status such as drowsy, lethargy. Age distribution of patients with neurologic complication was from 1 month to 5 years(mean 16.4 months). There were 30 boys and 22 girls. Among 14 patients who had seizure, 2 patients had convulsion disorder, 3 patients had febrile convulsion, and 9 patients had no seizure before. Their age distribution was from 1 months to 4 years(mean 18.4 months) and there were 8 boys and 6 girls. All of 14 patients showed generalized tonic clonic seizure and durations were from 30sec to 15 min(mean 4.4 min). All of the investigations were normal. None of 52 patients had recurrence and neurologic sequelae. CONCLUSION: It is important to know that RSV can cause CNS complications because we know how many patients infected by RSV. RSV-related seizure can be considered as benign seizure which does not need any long-term antiepileptic medication.
Age Distribution ; Child ; Humans ; Incidence ; Lethargy ; Medical Records ; Pediatrics ; Recurrence ; Respiratory Syncytial Viruses ; Seizures ; Seizures, Febrile

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). Results: The AI model showed an AUROC of 0.922 (95% CI, 0.842–0.969) in the internal test set and 0.870 (95% CI, 0.785–0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%–92.0%) and specificity of 91.3% (95% CI, 79.2%–97.6%) for the internal test set and 78.9% (95% CI, 54.4%–93.9%) and 88.2% (95% CI, 78.7%– 94.4%), respectively, for the external test set. With the model’s assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020–0.168; p = 0.012) and 0.069 (95% CI, 0.002–0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074–0.090; p = 0.850). Conclusion A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.