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Sökning: WFRF:(Hong Sungmin)

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1.
  • Bonkhoff, Anna K, et al. (författare)
  • The relevance of rich club regions for functional outcome post-stroke is enhanced in women.
  • 2023
  • Ingår i: Human brain mapping. - : Wiley. - 1097-0193 .- 1065-9471. ; 44:4, s. 1579-1592
  • Tidskriftsartikel (refereegranskat)abstract
    • This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS>2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.
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2.
  • Bourached, Anthony, et al. (författare)
  • Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity
  • 2023
  • Ingår i: BRAIN COMMUNICATIONS. - 2632-1297. ; 6:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by similar to 20% when increasing the sample size 9x [maximum for 100 patients: 0.279 +/- 0.005 (R2, 95% confidence interval), 900 patients: 0.337 +/- 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes. Bourached et al. contrast linear and deep learning-based algorithms in their prediction performances of stroke severity depending on the training set sample sizes. They find that linear regression outperforms deep learning-based algorithms for smaller training samples comprising lesion location information of 100 patients, while deep learning excels in the case of larger samples (N = 900).
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3.
  • Bretzner, Martin, et al. (författare)
  • Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke.
  • 2023
  • Ingår i: Neurology. - 1526-632X .- 0028-3878. ; 100:8
  • Tidskriftsartikel (refereegranskat)abstract
    • While chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age." We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes.We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input.We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years, r = 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; all p-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes.T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.
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4.
  • Hong, Sungmin, et al. (författare)
  • Daylight, twilight, and night variation in road environment-related freeway traffic crashes in Korea
  • 2018
  • Konferensbidrag (refereegranskat)abstract
    • There have been numerous studies of traffic crashes that analyze the relationship of crashes with traffic conditions, the road geometry, and environment. The present paper aims to systematically investigate the possible differences in the effects of those variables during night, twilight or day. Previous studies show there is a relationship between traffic crash frequency, injury severity, and the time of day. Also, research shows that driving is different depending on the time of day. Recent work using a driving simulator has found that there is a significant driving speed differential between night and day and concludes that roadway geometric conditions that are safe during day may not be safe during nighttime driving. Much of the research that considers differences between night and day driving focuses on driver-specific characteristics such as drunk driving, young drivers, age and gender, sleepiness, but omits a systematic investigation of daytime vs. night differences in road environment variables.The main contribution of this paper is therefore an investigation of the effect of road environment conditions on crashes under different light conditions, using random parameter Poisson and negative binomial regressions which are estimated separately for each light condition (daytime, nighttime, twilight) and the whole 24-hour day.
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5.
  • Kim, Kiyong, et al. (författare)
  • The methodology for identifying aggressive driving using driving behavior patterns
  • 2018
  • Konferensbidrag (refereegranskat)abstract
    • Aggressive driving behaviors, such as reckless driving and driving for revenge (It is common called “Road Rage”), are a major factor affecting the traffic safety by threatening other drivers on the roads. An Aggressive Driver is defined as “Operates a motor vehicle in a selfish, bold or pushy manner, without regard for the rights or safety of the other users of the streets and highways” by the New York State Police. Fatal or big traffic accidents can be caused by driving behaviors of these drivers. Aggressive driving drivers, however, are very hard to control because the events occur randomly. Therefore, researches are needed to effectively manage for aggressive driving behaviors by understanding and analyzing the behaviors.Precht et al. (2017) identified the effect of driving anger on driving behavior based on naturalistic driving data. The results of their study showed that anger was related to aggressive driving behaviors, but was not related to driving error frequencies. Consequently, they mentioned the dangerous driving behaviors were significantly affected by anger. According to AAA Foundation for Traffic Safety (1997, 2016), more than 78% of U.S. drivers had driven at least one aggressive driving behavior at once in the past year. The most common aggressive behaviors were to drive tailgating another vehicle to show their anger or irritation. The majority of aggressive drivers were young male drivers between the ages of 19 and 39. Many researchers have studied the aggressive or anger driving and have warned the danger of aggressive driving. However, the methodology for managing road rage based on the driving behavior data of aggressive drivers have been studied rarely. Therefore, the objective of the present study is to develop the methodology for identifying aggressive driving behavior patterns based on driving simulator experiments that by comparing behaviors between general driving and aggressive driving.
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