Death from motor vehicle accident is one of the most common causes of deaths in Korea, with 17.2 person per 100,000 population dying from motor vehicle accidents in 2004. Neuropsychiatric sequelae after motor vehicle accidents are very common and disturbing the quality of life. They include post-traumatic stress disorder, post-concussional disorder, organic personality disorder, depression, organic schizophrenia like disorder, malingering, factitious disorder, etc. Neuropsychiatric symptoms are often neglected by health care providers, although they tend to last for a long period as complex problems. They also may complicate the doctorpatient relationship. Therefore, early detection and management of neuropsychiatric symptoms are important for successful management of the patients. This article includes a brief summary of several neuropsychiatric states after motor vehicle accidents, especially post-traumatic stress disorder and a brief recommendation on legal reports for disability evaluation of mental and behavioral disorders after motor vehicle accidents.
Cause of Death ; Depression ; Disability Evaluation ; Disasters* ; Health Personnel ; Humans ; Korea ; Malingering ; Motor Vehicles* ; Personality Disorders ; Quality of Life ; Schizophrenia ; Stress Disorders, Post-Traumatic

OBJECTIVES: This study aimed to identify the characteristics of depression in early dementia and mild cognitive impairment patients. METHODS: We included 412 community-dwelling elderly. They were assessed with Mini-Mental Status Examination in the Korean version of the CERAD Assessment Packet (MMSE-KC), Clinical Dementia Rating Scale (CDR), Korean version of Geriatric Depression Scale (GDS) and Korean version of Hamilton Depression Rating Scale (HDRS). All patients were divided three groups, nondemented group (ND), mild cognitive impairment group (MCI), and early dementia group (ED). We compared depressive symptoms between three groups using each items of HDRS. RESULTS: Prevalence of depression (GDS> or =16) was 24.6% in ND, 33.3% in MCI and 41% in ED. Several items of HDRS, depressed mood, feeling of guilt, loss of work & interests, psychomotor retardation, psychomotor agitation, psychic anxiety, somatic anxiety, and gastrointestinal symptoms, were significantly associated with cognitive decline in all subjects. However, no item of HDRS was significantly associated with cognitive decline in depressive patients. CONCLUSION: This study suggests that the prevalence of depression may increase as cognitive function declines. There was no difference in depressive symptoms between three groups.
Aged ; Alzheimer Disease* ; Anxiety ; Dementia ; Depression* ; Guilt ; Humans ; Mild Cognitive Impairment* ; Prevalence* ; Psychomotor Agitation

Discal cysts, which lie directly over intervertebral discs, are rare. Two old dogs with tetraparesis were referred to our facility. In both animals, magnetic resonance imaging revealed intraspinal extradural cystic mass lesions that were dorsal to degenerative intervertebral discs at the C3-C4 level. These lesions had low signal intensity on T1-weighted images, and high signal intensity on T2-weighted images. A ventral slot approach was used to perform surgical decompression, after which the symptoms improved remarkably. Discal cysts should be included in the differential diagnosis of dogs with cervical pain and tetraparesis. One effective treatment for discal cysts is surgical intervention.
Animals ; Decompression, Surgical ; Diagnosis, Differential ; Dogs* ; Intervertebral Disc ; Intervertebral Disc Degeneration ; Magnetic Resonance Imaging ; Neck Pain ; Spine*

BACKGROUND/AIMS: Various strategies to expand the ablation zone have been attempted using hepatic radiofrequency ablation (RFA). The optimal strategy, however, is unknown. We compared hepatic RFA with an internally cooled wet (ICW) electrode and vascular inflow occlusion. METHODS: Eight dogs were assigned to one of three groups: only RFA using an internally cooled electrode (group A), RFA using an ICW electrode (group B), and RFA using an internally cooled electrode with the Pringle maneuver (group C). The ablation zone diameters were measured on the gross specimens, and the volume of the ablation zone was calculated. RESULTS: The ablation zone volume was greatest in group B (1.82+/-1.23 cm3), followed by group C (1.22+/-0.47 cm3), and then group A (0.48+/-0.33 cm3). The volumes for group B were significantly larger than the volumes for group A (p=0.030). There was no significant difference in the volumes between groups A and C (p=0.079) and between groups B and C (p=0.827). CONCLUSIONS: Both the usage of an ICW electrode and hepatic vascular occlusion effectively expanded the ablation zone. The use of an ICW electrode induced a larger ablation zone with easy handling compared with using hepatic vascular occlusion, although this difference was not statistically significant.
Animals ; Catheter Ablation ; Dogs ; Electrodes ; Handling (Psychology) ; Liver

Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

BACKGROUND: Adrenal venous sampling (AVS) is a gold standard for subtype classification of primary aldosteronism (PA). However, this procedure has a high failure rate because of the anatomical difficulties in accessing the right adrenal vein. We investigated whether C-arm computed tomography-assisted AVS (C-AVS) could improve the success rate of adrenal sampling. METHODS: A total of 156 patients, diagnosed with PA who underwent AVS from May 2004 through April 2017, were included. Based on the medical records, we retrospectively compared the overall, left, and right catheterization success rates of adrenal veins during the periods without C-AVS (2004 to 2010, n=32) and with C-AVS (2011 to 2016, n=124). The primary outcome was adequate bilateral sampling defined as a selectivity index (SI) >5. RESULTS: With C-AVS, the rates of adequate bilateral AVS increased from 40.6% to 88.7% (P<0.001), with substantial decreases in failure rates (43.7% to 0.8%, P<0.001). There were significant increases in adequate sampling rates from right (43.7% to 91.9%, P<0.001) and left adrenal veins (53.1% to 95.9%, P<0.001) as well as decreases in catheterization failure from right adrenal vein (9.3% to 0.0%, P<0.001). Net improvement of SI on right side remained significant after adjustment for left side (adjusted SI, 1.1 to 9.0; P=0.038). C-AVS was an independent predictor of adequate bilateral sampling in the multivariate model (odds ratio, 9.01; P<0.001). CONCLUSION: C-AVS improved the overall success rate of AVS, possibly as a result of better catheterization of right adrenal vein.
Adrenalectomy ; Catheterization* ; Catheters ; Classification ; Cone-Beam Computed Tomography ; Humans ; Hyperaldosteronism* ; Hypertension ; Medical Records ; Retrospective Studies ; Veins*

Objective: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA). Materials and Methods: Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions. Results: Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of “pre-PTA” shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, GradCAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram. Conclusion Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.