Recent advancements in artificial intelligence (AI), especially in deep learning algorithms, have driven significant innovations across numerous industries, including medicine. Neuroimaging, faced with challenges from frequent acute neurological conditions and a rising prevalence of neurodegenerative disorders, has become an active field where AI is increasingly integrated into clinical workflows.Current Concepts: In acute neurological disorders, AI models have been developed to improve the diagnostic accuracy of computed tomography and magnetic resonance imaging in detecting acute intracerebral hemorrhage and ischemic stroke. These systems expedite lesion identification, assist in patient triaging, and predict critical outcomes such as hematoma expansion from imaging features. Similarly, in neurodegenerative diseases such as Alzheimer dementia and Parkinson disease, AI enhances quantitative assessment of brain atrophy and identifies subtle imaging alterations that are challenging to detect visually. These AI solutions are now commercially available and already integrated into clinical practice. Surveys among neuroradiologists indicate growing acceptance of AI, acknowledging its potential to decrease workload and enhance clinical decision-making.Discussion and Conclusion: Despite these promising advancements, clinical adoption faces challenges due to the need for standardized imaging protocols and AI systems capable of revealing new insights from conventional studies. Future efforts should focus on integrating AI into existing diagnostic workflows to provide innovative diagnostic insights, paving the way for personalized and effective patient care.

Recent advancements in artificial intelligence (AI), especially in deep learning algorithms, have driven significant innovations across numerous industries, including medicine. Neuroimaging, faced with challenges from frequent acute neurological conditions and a rising prevalence of neurodegenerative disorders, has become an active field where AI is increasingly integrated into clinical workflows.Current Concepts: In acute neurological disorders, AI models have been developed to improve the diagnostic accuracy of computed tomography and magnetic resonance imaging in detecting acute intracerebral hemorrhage and ischemic stroke. These systems expedite lesion identification, assist in patient triaging, and predict critical outcomes such as hematoma expansion from imaging features. Similarly, in neurodegenerative diseases such as Alzheimer dementia and Parkinson disease, AI enhances quantitative assessment of brain atrophy and identifies subtle imaging alterations that are challenging to detect visually. These AI solutions are now commercially available and already integrated into clinical practice. Surveys among neuroradiologists indicate growing acceptance of AI, acknowledging its potential to decrease workload and enhance clinical decision-making.Discussion and Conclusion: Despite these promising advancements, clinical adoption faces challenges due to the need for standardized imaging protocols and AI systems capable of revealing new insights from conventional studies. Future efforts should focus on integrating AI into existing diagnostic workflows to provide innovative diagnostic insights, paving the way for personalized and effective patient care.

Recent advancements in artificial intelligence (AI), especially in deep learning algorithms, have driven significant innovations across numerous industries, including medicine. Neuroimaging, faced with challenges from frequent acute neurological conditions and a rising prevalence of neurodegenerative disorders, has become an active field where AI is increasingly integrated into clinical workflows.Current Concepts: In acute neurological disorders, AI models have been developed to improve the diagnostic accuracy of computed tomography and magnetic resonance imaging in detecting acute intracerebral hemorrhage and ischemic stroke. These systems expedite lesion identification, assist in patient triaging, and predict critical outcomes such as hematoma expansion from imaging features. Similarly, in neurodegenerative diseases such as Alzheimer dementia and Parkinson disease, AI enhances quantitative assessment of brain atrophy and identifies subtle imaging alterations that are challenging to detect visually. These AI solutions are now commercially available and already integrated into clinical practice. Surveys among neuroradiologists indicate growing acceptance of AI, acknowledging its potential to decrease workload and enhance clinical decision-making.Discussion and Conclusion: Despite these promising advancements, clinical adoption faces challenges due to the need for standardized imaging protocols and AI systems capable of revealing new insights from conventional studies. Future efforts should focus on integrating AI into existing diagnostic workflows to provide innovative diagnostic insights, paving the way for personalized and effective patient care.

Objectives: The purpose of this study was to develop a screening tool that is simple and easy to use for assessing sleep problems, including hypersomnolence, restless legs syndrome, and insomnia. We also examined the reliability and validity of this tool. Methods: We developed the Sleep Problem Screening Questionnaire (SPSQ), which consists of three sub-sections: insomnia (SPSQi), hypersomnolence (SPSQh), and restless legs syndrome (SPSQr). Subsequently, the participants, consisting of 222 patients with insomnia disorder and 78 healthy individuals, completed both the SPSQ and the comparative scale (Korean version of the Insomnia Severity Index). The analysis was then conducted using this data. Results: The SPSQ demonstrated good convergent and discriminant validity, as well as satisfactory internal consistency. A cutoff score of 6 on the SPSQi was found to be optimal for distinguishing individuals with insomnia. Conclusion The results of this study suggest that the SPSQ is a reliable and valid tool for screening sleep problems among general adult population. However, there is a limitation as a comparison and validation with scales related to restless legs syndrome and hypersomnolence were not conducted.

This study aimed to compare the pharmacokinetic (PK) and safety profiles of 2 fenofibric acid formulations under fasting and fed conditions. The reference was a 135 mg capsule, while the test was a 110 mg enteric-coated tablet. This randomized, open-label, two-sequence, two-period crossover phase 1 clinical trial was conducted in healthy Korean men. Sixty participants were enrolled in each of the fasting and feeding groups. Blood samples were collected 72 hours after drug administration. PK parameters were calculated using a noncompartmental method with Phoenix WinNonlin ® . A total of 53 and 51 participants from the fasting and feeding groups, respectively, completed the study. The geometric mean ratio and 90% confidence intervals of the maximum concentration (C max ) and area under the concentration-time curve to the last measurable plasma concentration were 0.9195 (0.8795–0.9614) and 0.8630 (0.8472–0.8791) in the fasting study and 1.0926 (1.0102–1.1818) and 0.9998 (0.9675–1.0332) in the fed study, respectively. The time to reach C max of the enteric-coated tablet compared to that of the capsule was extended by 1 and 3 hours under fasting and fed conditions, respectively. In conclusion, enteric-coated tablets have a higher bioavailability than capsules. In addition, the enteric-coated tablet was smaller than the capsule, making it easier for patients to swallow.

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.

The general linear model (GLM) describes the dependent variable as a linear combination of independent variables and an error term. The GLM procedure of SAS® and the “car” package in R calculate the type I, II, or III ANOVA (analysis of variance) tables. In this study, we validated the newly-developed R package, “sasLM,” which is compatible with the GLM procedure of SAS®. The “sasLM” package was validated by comparing the output with SAS®, which is the current gold standard for statistical programming. Data from ten books and articles were used for validation. The results of the “sasLM” and “car” packages were compared with those in SAS® using 194 models. All of the results in “sasLM” were identical to those of SAS®, whereas more than 20 models in “car” showed different results from those of SAS®. As the results of the “sasLM” package were similar to those in SAS® PROC GLM, the “sasLM” package could be a viable alternative method for calculating the type II and III sum of squares. The newly-developed “sasLM” package is free and open-source, therefore it can be used to develop other useful packages as well. We hope that the “sasLM” package will enable researchers to conveniently analyze linear models.

PURPOSE: Although endovascular treatment is currently thought to only be suitable for patients who have pial arterial filling scores >3 as determined by multiphase computed tomography angiography (mpCTA), a cut-off score of 3 was determined by a study, including patients within 12 hours after symptom onset. We aimed to investigate whether a cut-off score of 3 for endovascular treatment within 6 hours of symptom onset is an appropriate predictor of good functional outcome at 3 months. MATERIALS AND METHODS: From April 2015 to January 2016, acute ischemic stroke patients treated with mechanical thrombectomy within 6 hours of symptom onset were enrolled into this study. Pial arterial filling scores were semi-quantitatively assessed using mpCTA, and clinical and radiological parameters were compared between patients with favorable and unfavorable outcomes. Multivariate logistic regression analysis was then performed to investigate the independent association between clinical outcome and pial collateral score, with the predictive power of the latter assessed using C-statistics. RESULTS: Of the 38 patients enrolled, 20 (52.6%) had a favorable outcome and 18 had an unfavorable outcome, with the latter group showing a lower mean pial arterial filling score (3.6±0.8 vs. 2.4±1.2, P=0.002). After adjusting for variables with a P-value of <0.1 in univariate analysis (i.e., age and National Institutes of Health Stroke Scale score at admission), pial arterial filling scores higher than a cut-off of 2 were found to be independently associated with favorable clinical outcomes (P=0.012). C-statistic analysis confirmed that our model had the highest prediction power when pial arterial filling scores were dichotomized at >2 vs. ≤2. CONCLUSION: A pial arterial filling cut-off score of 2 as determined by mpCTA appears to be more suitable for predicting clinical outcomes following endovascular treatment within 6 hours of symptom onset than the cut-off of 3 that had been previously suggested.
Angiography ; Humans ; Logistic Models ; National Institutes of Health (U.S.) ; Stroke ; Thrombectomy

BACKGROUND AND PURPOSE: This retrospective cross-sectional study included 18 patients from unrelated families harboring mutations of the transthyretin gene (TTR), and analyzed their characteristics and geographical distribution in South Korea. METHODS: The included patients had a diagnosis of systemic amyloidosis, clinical symptoms, such as amyloid neuropathy or cardiomyopathy, and confirmation of a TTR gene mutation using genetic analysis recorded between April 1995 and November 2014. RESULTS: The mean age at disease onset was 49.6 years, and the mean disease duration from symptom onset to diagnosis was 3.67 years. Fifteen of the 18 patients were classified as mixed phenotype, 2 as the neurological phenotype, and only 1 patient as the cardiac phenotype. The most-common mutation pattern in South Korea was Asp38Ala, which was detected in eight patients. Thirteen patients reported their family hometowns, and five of the eight harboring the Asp38Ala mutation were from the Gyeongsang province in southeast Korea. The other eight patients exhibited a widespread geographical distribution. A particularly noteworthy finding was that the valine at position 30 (Val30Met) mutation, which was previously reported as the most-common TTR mutation worldwide and also the most common in the Japanese population, was not detected in the present South Korean patients. CONCLUSIONS: South Korean patients with hereditary TTR amyloidosis exhibited heterogeneous TTR genotypes and clinical phenotypes. The findings of this study suggest that the distribution of TTR amyloidosis in South Korea is due to de novo mutations and/or related to the other countries in East Asia.
Amyloid Neuropathies ; Amyloidosis* ; Asian Continental Ancestry Group ; Cardiomyopathies ; Cross-Sectional Studies ; Diagnosis ; Far East ; Genotype ; Humans ; Korea ; Phenotype ; Prealbumin* ; Retrospective Studies ; Valine

OBJECTIVE: To evaluate the utility of high-resolution vessel wall imaging (HR-VWI) of middle cerebral artery (MCA), and to compare HR-VWI findings between striatocapsular infarction (SC-I) and lenticulostriate infarction (LS-I). MATERIALS AND METHODS: This retrospective study was approved by the Institutional Review Board, and informed consent was waived. From July 2009 to February 2012, 145 consecutive patients with deep subcortical infarctions (SC-I, n = 81; LS-I, n = 64) who underwent HR-VWI were included in this study. The degree of MCA stenosis and the characteristics of MCA plaque (presence, eccentricity, location, extent, T2-high signal intensity [T2-HSI], and plaque enhancement) were analyzed, and compared between SC-I and LS-I, using Fisher's exact test. RESULTS: Stenosis was more severe in SC-I than in LS-I (p = 0.040). MCA plaque was more frequent in SC-I than in LS-I (p = 0.028), having larger plaque extent (p = 0.001), more T2-HSI (p = 0.001), and more plaque enhancement (p = 0.002). The eccentricity and location of the plaque showed no significant difference between the two groups. CONCLUSION: Both SC-I and LS-I have similar HR-VWI findings of the MCA plaque, but SC-I had more frequent, larger plaques with greater T2-HSI and enhancement. This suggests that HR-VWI may have a promising role in assisting the differentiation of underlying pathophysiological mechanism between SC-I and LS-I.
Cerebral Infarction* ; Constriction, Pathologic ; Ethics Committees, Research ; Humans ; Infarction ; Informed Consent ; Magnetic Resonance Imaging* ; Middle Cerebral Artery* ; Retrospective Studies ; Stroke