Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95–100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

Background: The human brain structure undergoes considerable changes throughout life. Cognitive function can be affected either negatively or positively. It is challenging to segregate normal brain aging from the accelerated one.Objective: To work out a descriptive model of brain structural and functional changes in normal aging.Materials and Methods: By using voxel-based morphometry and lesion segmentation along with linear statistics and machine learning (ML), we analyzed the structural changes in the major brain compartments and modeled the dynamics of neurofunctional performance throughout life. We studied sex differences in lifelong dynamics of brain volumetric data with Mann-Whitney U-test. We tested the hypothesis that performance in some cognitive domains might decline as a linear function of age while other domains might have a non-linear dependence on it. We compared the volumetric changes in the major brain compartments with the dynamics of psychophysiological performance in 4 age groups. Then, we tested linear models of structural and functional decline for significant differences between the slopes in age groups with the T-test.Results: White matter hyperintensities (WMH) are not the major structural determinant of the brain normal aging. They should be viewed as signs of a disease. There is a sex difference in the speed and/or in the onset of the gray matter atrophy. It either starts earlier or goes faster in males. Marked sex difference in the proportion of total cerebrospinal fluid (CSF) and intraventricular CSF (iCSF) justifies that elderly men are more prone to age-related brain atrophy than women of the same age.Conclusion: The article gives an overview and description of the conceptual structural changes in the brain compartments. The obtained data justify distinct patterns of age-related changes in the cognitive functions. Cross-life slowing of decision-making may follow the linear tendency of enlargement of the interhemispheric fissure because the center of task switching and inhibitory control is allocated within the medial wall of the frontal cortex, and its atrophy accounts for the expansion of the fissure. Free online tool at https://med-predict.com illustrates the tests and study results.

Tuberculosis of the heart is relatively rare and presents a significant diagnostic difficulty for physicians. It is the leading cause of death from infectious illness. It is one of the top 10 leading causes of death worldwide, with a disproportionate impact in low- and middle-income nations. The radiologist plays a pivotal role as CMR is a non-invasive radiological method that can aid in identifying potential overlap and differential diagnosis between tuberculosis, mass lesions, pericarditis, and myocarditis. Regardless of similarities or overlap in observations, the combination of clinical and certain particular radiological features, which are also detected by comparison to earlier and follow-up CMR scans, may aid in the differential diagnosis. CMR offers a significant advantage over echocardiography for detecting, characterizing, and assessing cardiovascular abnormalities. In conjunction with clinical presentation, knowledge of LGE, feature tracking, and parametric imaging in CMR may help in the early detection of tuberculous myopericarditis and serve as a surrogate for endomyocardial biopsy resulting in a quicker diagnosis and therapy. This article aims to explain the current state of cardiac tuberculosis, the diagnostic utility of CMR in tuberculosis (TB) patients, and offer an overview of the various imaging and laboratory procedures used to detect cardiac tuberculosis.

A high incidence and prevalence of neurodegenerative diseases and neurodevelopmental disorders justify the necessity of well-defined criteria for diagnosing these pathologies from brain imaging findings. No easy-to-apply quantitative markers of abnormal brain development and ageing are available. We aim to find the characteristic features of non-pathological development and degeneration in distinct brain structures and to work out a precise descriptive model of brain morphometry in age groups. We will use four biomedical databases to acquire original peer-reviewed publications on brain structural changes occurring throughout the human life-span. Selected publications will be uploaded to Covidence systematic review software for automatic deduplication and blinded screening. Afterwards, we will manually review the titles, abstracts, and full texts to identify the papers matching eligibility criteria. The relevant data will be extracted to a ‘Summary of findings’ table. This will allow us to calculate the annual rate of change in the volume or thickness of brain structures and to model the lifelong dynamics in the morphometry data. Finally, we will adjust the loss of weight/thickness in specific brain areas to the total intracranial volume. The systematic review will synthesise knowledge on structural brain change across the life-span.

Bedside diagnostic ultrasound for traumatic pneumothorax is easy and reliable. However, the thoracic anatomical locations to be examined are debateable. We aimed to study the anatomical locations of blunt traumatic pneumothoraces as defined by chest CT scan to identify the areas that should be scanned while performing bedside diagnostic ultrasound. This is a retrospective analysis of a data collected for a previous study in blunt trauma patients at our hospital during a 4-year-period with CT confirmed pneumothoraces. The anatomical distribution of the pneumothoraces and their volume were analyzed. Advanced statistical analysis was performed using repeated measures logistic regression models. Seven hundred three patients had a CT scan of the chest. Seventy-four patients (10.5%) were confirmed to have a pneumothorax. Only 64 were included in the study as they did not have a chest tube inserted before the CT scan. Twelve (18.8%) patients had bilateral pneumothorax. Seventy-six pneumothoraces were identified for which 41 patients had a right-sided pneumothorax and 35 patients had a left-sided pneumothorax. 95.1 % of the pneumothoraces detected on the right side were in the whole parasternal area with 75.6% seen in the lower parasternal region only. Similarly, 97.1 % of the pneumothoraces on the left side were seen in the whole parasternal area with 80% seen in the lower parasternal region only. The current study showed that air pockets of blunt traumatic pneumothoraces are mainly located at the parasternal regions especially in pneumothorax with small volume. We recommend a quick ultrasound scanning of the parasternal regions on both sides of the chest from proximal to distal as the appropriate technique for the detection of pneumothoraces in blunt trauma setting.

Background: Recently, rapid fluctuations of ambient temperature were found to be associated with hospital admission for cardiovascular diseases in general and the ischemic stroke in particular. Objective: to test if climatic factors predict the incidence of stroke reliably and to study the predictive potential of risk factors for a stroke. Materials and methods: In a retrospective design, we studied 566 patients admitted to the stroke unit in 2016-2019. A distributed lag nonlinear model was used to explore immediate and delayed effects of weather and clinicodemographic risk factors on the stroke incidence. Supervised machine learning was used to build models predictive of the mRS score. We assessed model performance by calculating $\text{R}^{2}$ , mean absolute error and root-mean-square error. Results and conclusions: We found a non-correlation between the weather parameters and statistics on stroke. The disparities in their trends lead us to investigate behind time effects of the environment with distributed lag models and a concordant impact of all the settings - with machine learning models. If categorized into two classes by severity and functional outcomes, the cases have few disparities in the weather parameters within a week before the stroke onset. In contrast to the groups classified by severity, the ones grouped by outcomes have a significant difference in age, nationality, the presence of background diseases and smoking status. We ranked environmental and individual risk factors by the information gain that they provide to the models. Inclusion of the weather parameters into the machine learning model predicting the mRS score provides a slight boost in performance.