BACKGROUND: Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling. Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility. Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns. METHODS: We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex. The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics. The model's performance was compared against four expert clinicians using 14 previously unseen MRI scans. RESULTS: Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs. The model achieved a balanced accuracy of 64.8% ± 3.4%, with a weighted top-3 accuracy of 84.7% ± 1.8% and top-5 accuracy of 90.2% ± 2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making. Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0% ± 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. CONCLUSIONS: The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques. Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform.

• MeSH
• dospělí MeSH
• internet MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• neuromuskulární nemoci * diagnóza   diagnostické zobrazování   MeSH
• strojové učení * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

PURPOSE: Epilepsy in children is often associated with impaired quality of life, lower academic achievement, and reduced academic self-concept, as well as an increased risk of depression and anxiety. This study aims to evaluate the possible impact of comorbidities, such as learning disabilities (LD) and attention deficit hyperactivity disorder (ADHD), on these variables. METHODS: A total of 104 children with epilepsy (CWE) aged 8-15 years, attending mainstream schools, participated in the study. Of these, 45 were diagnosed with LD and/or ADHD. Participants completed the CHEQOL-25 questionnaire to assess quality of life (QoL), the SPAS questionnaire to evaluate academic self-concept, as well as inventories measuring depressive and anxiety symptoms. The data were analyzed to identify differences between subgroups with and without LD/ADHD using a two-sample t-test. Additionally, correlation analysis was conducted to identify other relevant variables influencing QoL, academic self-concept, and depressive and anxiety symptoms. RESULTS: QoL and academic self-concept were significantly poorer in CWE with LD/ADHD compared to those without comorbidities. QoL showed statistically significant associations with depressive and anxiety symptoms, and academic self-concept. While depressive symptoms levels in CWE without comorbidities align with those in the general population, CWE with LD/ADHD showed an increased association with depressive symptoms. Although anxiety symptoms were relatively strongly associated with depressive symptoms, their prevalence remains broadly comparable to that of children without epilepsy, regardless of the presence of LD/ADHD. CONCLUSION: CWE with LD/ADHD and their families may benefit from focused attention, including targeted counseling and therapeutic interventions. However, specific interventional studies are recommended, based on child-specific findings.

• MeSH
• deprese epidemiologie   psychologie   MeSH
• dítě MeSH
• duševní zdraví * MeSH
• epilepsie * psychologie   epidemiologie   komplikace   MeSH
• hyperkinetická porucha epidemiologie   psychologie   MeSH
• komorbidita MeSH
• kvalita života * psychologie   MeSH
• lidé MeSH
• mladiství MeSH
• poruchy učení * epidemiologie   psychologie   MeSH
• průzkumy a dotazníky MeSH
• sebepojetí * MeSH
• úzkost epidemiologie   psychologie   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major challenge, particularly pertinent for 'actionable' genes such as thyroid hormone transporter MCT8 (encoded by the X-linked SLC16A2 gene), where loss-of-function (LoF) variants cause a rare neurodevelopmental and (treatable) metabolic disorder in males. The combination of deep phenotyping data with functional and computational tests and with outcomes in population cohorts, enabled us to: (i) identify the genetic aetiology of divergent clinical phenotypes of MCT8 deficiency with genotype-phenotype relationships present across survival and 24 out of 32 disease features; (ii) demonstrate a mild phenocopy in ~400,000 individuals with common genetic variants in MCT8; (iii) assess therapeutic effectiveness, which did not differ among LoF-categories; (iv) advance structural insights in normal and mutated MCT8 by delineating seven critical functional domains; (v) create a pathogenicity-severity MCT8 variant classifier that accurately predicted pathogenicity (AUC:0.91) and severity (AUC:0.86) for 8151 variants. Our information-dense mapping provides a generalizable approach to advance multiple dimensions of rare genetic disorders.

• MeSH
• deep learning * MeSH
• dítě MeSH
• dospělí MeSH
• fenotyp * MeSH
• genetická variace MeSH
• genetické asociační studie MeSH
• genomika metody   MeSH
• hormony štítné žlázy metabolismus   genetika   MeSH
• lidé MeSH
• mentální retardace vázaná na chromozom X genetika   metabolismus   MeSH
• mladiství MeSH
• mutace ztráty funkce MeSH
• předškolní dítě MeSH
• přenašeče monokarboxylových kyselin * genetika   metabolismus   MeSH
• stupeň závažnosti nemoci MeSH
• svalová atrofie genetika   metabolismus   patologie   MeSH
• svalová hypotonie genetika   metabolismus   MeSH
• symportéry * genetika   metabolismus   MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Prognostic machine learning research in multiple sclerosis has been mainly focusing on black-box models predicting whether a patients' disability will progress in a fixed number of years. However, as this is a binary yes/no question, it cannot take individual disease severity into account. Therefore, in this work we propose to model the time to disease progression instead. Additionally, we use explainable machine learning techniques to make the model outputs more interpretable. METHODS: A preprocessed subset of 29,201 patients of the international data registry MSBase was used. Disability was assessed in terms of the Expanded Disability Status Scale (EDSS). We predict the time to significant and confirmed disability progression using random survival forests, a machine learning model for survival analysis. Performance is evaluated on a time-dependent area under the receiver operating characteristic and the precision-recall curves. Importantly, predictions are then explained using SHAP and Bellatrex, two explainability toolboxes, and lead to both global (population-wide) as well as local (patient visit-specific) insights. RESULTS: On the task of predicting progression in 2 years, the random survival forest achieves state-of-the-art performance, comparable to previous work employing a random forest. However, here the random survival forest has the added advantage of being able to predict progression over a longer time horizon, with AUROC >60% for the first 10 years after baseline. Explainability techniques further validated the model by extracting clinically valid insights from the predictions made by the model. For example, a clear decline in the per-visit probability of progression is observed in more recent years since 2012, likely reflecting globally increasing use of more effective MS therapies. CONCLUSION: The binary classification models found in the literature can be extended to a time-to-event setting without loss of performance, thus allowing a more comprehensive prediction of patient prognosis. Furthermore, explainability techniques proved to be key to reach a better understanding of the model and increase validation of its behaviour.

• MeSH
• algoritmy MeSH
• časové faktory MeSH
• dospělí MeSH
• lidé MeSH
• prognóza MeSH
• progrese nemoci * MeSH
• registrace MeSH
• ROC křivka MeSH
• roztroušená skleróza * patofyziologie   MeSH
• strojové učení * MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Despite the widespread use of the Movement Assessment Battery for Children, 2nd edition (MABC-2), little is known about the sensitivity or specificity of the individual items to detect probable Developmental Coordination Disorder (p-DCD). This study examined which specific MABC-2 items were most sensitive to identify children with p-DCD and which items would predict p-DCD. METHODS: Based on a large dataset including European and African children aged 3-16 years (n = 4916, typically developing (TD, 49.6 % boys); n = 822 p-DCD (53.1 % boys), Hedges' g was calculated to establish the standardized mean difference (SMD) between p-DCD/TD. SMDs were considered substantial when absolute values at or above 1.4. Sensitivity and specificity of the raw MABC-2 item scores predicting p-DCD/TD per age band (AB) were established with logistic regression analysis. RESULTS: AB1: Children with p-DCD performed substantially poorer on threading beads (SMD: -1.61) and jumping on mats (SMD: 1.61). By combining all items and the country of origin, the sensitivity was 61.7 % and specificity 98.6 %. AB2: Walking heel-to-toe forwards (SMD: 1.65) was substantially poorer in p-DCD. By combining all items and the country of origin, the sensitivity was 79.0 % and specificity 97.6 %. AB3: Catching a ball with the preferred (SMD: 1.8) or non-preferred (SMD: 1.61) hand, and for walking heel-to-toe backwards (SMD: 1.78) were substantially poorer in p-DCD. All items combined resulted in a sensitivity of 94.4 % and specificity of 99.6 %. CONCLUSION: Not all MABC-2 items are equally sensitive to distinguish between performances of p-DCD and TD. Despite the good specificity, the sensitivity was only moderate in AB1-2, the age at which children learn culturally influenced motor skills.

• MeSH
• dítě MeSH
• lidé MeSH
• logistické modely MeSH
• mladiství MeSH
• motorické dovednosti MeSH
• pohyb MeSH
• poruchy motorických dovedností * diagnóza   MeSH
• předškolní dítě MeSH
• senzitivita a specificita * MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Afrika MeSH
• Evropa MeSH

Přehledová studie se věnuje tématu opožděného vývoje jazykových schopností (dále OVJ), který představuje rané opoždění v jazykovém vývoji. Toto rané opoždění může být symptomem některých neurovývojových poruch (nejčastěji poruchy autistického spektra, vývojové poruchy intelektu, vývojové poruchy jazyka a vývojové poruchy učení), nebo může v průběhu vývoje spontánně vymizet. Druhá varianta vývojové trajektorie se v komunitě českých klinických logopedů označuje jako OVJ prostý. V českém prostředí je na jedné straně upozorňováno na nadužívání tohoto termínu, na druhé straně zde postrádáme informace, které by danou kategorii jasněji ohraničily a vymezily situace, kdy můžeme označovat potíže v jazykovém vývoji tímto termínem. Tato studie proto podává přehled dosavadních poznatků, díky nimž můžeme vymezit kvalitu jazykových obtíží a věkové rozmezí, které lze k OVJ vztahovat. Dále na podkladě zahraničních výzkumů informuje o prevalenci, etiologii a diagnostice OVJ a jeho vztahu k neurovývojovým poruchám. Jsou zde popsány také konkrétní diagnostické postupy pro identifikaci OVJ v raném věku a pro odlišení OVJ prostého od OVJ doprovázejícího neurovývojové poruchy. Zvláštní prostor je v tomto směru věnován vývojové poruše jazyka v souvislosti s diagnostickými markery, které představují spolehlivý nástroj diferenciální diagnostiky, což je doloženo i řadou longitudinálních studií.

The review study focuses on the topic of language delay (LD), which represents an early delay in language development. This early delay may be a symptom of certain neurodevelopmental disorders (autism spectrum disorders, developmental intellectual disabilities, developmental language disorders, developmental learning disorders) or may spontaneously disappear during development. The second variant of the developmental trajectory is referred to in the Czech clinical speech therapy community as simple language delay. In the Czech environment, on the one hand, the overuse of this term is pointed out; on the other hand, there is a lack of information that would more clearly delimit the category and define the situations in which we can use this term to refer to difficulties in the language development. Therefore, this study provides an overview of the existing knowledge that allows us to define the quality of language difficulties and the age range that can be related to the LD. It also reports on the prevalence, aetiology and diagnosis of LD and its relationship to neurodevelopmental disorders based on international research. It also describes specific diagnostic procedures for identifying LD in early life and for distinguishing simple LD from LD accompanying neurodevelopmental disorders. A special section of the text is devoted to developmental language disorders in the context of diagnostic markers that represent a reliable tool for differential diagnosis, as evidenced by longitudinal studies.

• MeSH
• diferenciální diagnóza MeSH
• dítě MeSH
• lidé MeSH
• neurovývojové poruchy MeSH
• vývojové poruchy řeči * diagnóza   etiologie   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• Publikační typ
• přehledy MeSH

BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. METHODS: In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). RESULTS: We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). DISCUSSION: The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

• MeSH
• deep learning * MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• longitudinální studie MeSH
• magnetická rezonanční tomografie * MeSH
• mozek * diagnostické zobrazování   patologie   MeSH
• neurodegenerativní nemoci diagnostické zobrazování   MeSH
• průřezové studie MeSH
• retrospektivní studie MeSH
• roztroušená skleróza * diagnostické zobrazování   patologie   MeSH
• stárnutí * patologie   fyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH

BACKGROUND: An association between lipid measures and cognitive decline in patients with multiple sclerosis (MS) has been suggested. OBJECTIVES: This study aimed to investigate relationships between lipid profile and cognitive performance in a large observational cohort of MS patients. MATERIALS AND METHODS: We included 211 patients with 316 available pairs of lipid and cognitive measures performed over follow-up. The time between lipid and cognitive measures did not exceed 90 days. Baseline data were analyzed by non-parametric Spearman rank correlation test. Repeated measures were analyzed using linear mixed models adjusted for sex, age, education level, disease-modifying therapy status, and depression. RESULTS: Baseline analyses showed a correlation between higher low-density lipoprotein cholesterol (LDL-C) and lower Categorical Verbal Learning Test (CVLT) (rho=-0.15; p = 0.04), lower Symbol Digit Modalities Test (SDMT) (rho=-0.16; p = 0.02) and lower Brief Visuospatial Memory Test-Revised (BVMT-R) scores (rho=-0.12; p = 0.04). Higher high-density lipoprotein cholesterol (HDL-C) was negatively correlated with lower SDMT scores (rho=-0.16; p = 0.02) and lower Paced Auditory Serial Addition Test-3 (PASAT-3) scores (rho=-0.24; p = 0.03). Mixed model analyses of repeated measures showed a negative association between higher LDL-C and lower CVLT (B=-0.02; p < 0.001, Cohen ́s d = 0.08) and lower BVMT-R (B=-0.01; p = 0.03, Cohen ́s d=-0.12). Also, the negative association between HDL-C and PASAT-3 was confirmed in the mixed model analysis (B=-0.18; p = 0.01, Cohen ́s d = 0.07). Additional adjustments of the models for disability assessed by Expanded Disability Status Scale or Normalized Brain Volume did not change the results of the models substantially. CONCLUSIONS: Our results suggest a mild negative impact of dyslipidemia on cognitive performance in patients with MS. We propose that dyslipidemia contributes, at least in part, to cognitive decline in MS patients, independent of brain atrophy.

• MeSH
• dospělí MeSH
• HDL-cholesterol krev   MeSH
• kognice fyziologie   MeSH
• kognitivní dysfunkce * etiologie   krev   patofyziologie   MeSH
• LDL-cholesterol * krev   MeSH
• lidé středního věku MeSH
• lidé MeSH
• neuropsychologické testy MeSH
• roztroušená skleróza * krev   komplikace   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• pozorovací studie MeSH

Cévní mozková příhoda je jednou z nejčastějších příčin získané disability. Představuje závažný socioekonomický problém, který může mít závažný dopad na různé oblasti života. Včasná a dostatečně intenzivní rehabilitace po CMP významně přispívá k optimálním funkčním výsledkům a zlepšení kvality života pacientů. Nové neurorehabilitační přístupy založené na technologiích a virtuální realitě (VR) umožňují navrhnout individualizovaný intenzivní rehabilitační trénink a zlepšit motorické učení prostřednictvím multimodální zpětné vazby. Rehabilitace ve VR je vysoce motivující terapie s řadou výhod pro pacienta a zvyšuje také compliance pacienta k terapii. Představuje bezpečnou formu terapie a po náležitém edukování pacienta není nezbytně nutná fyzická přítomnost fyzioterapeuta. Díky tomu je možné využití VR v domácím cvičení a telerehabilitaci. Cílem tohoto přehledového článku je poskytnout aktuální poznatky a stručné informace o neurorehabilitaci po CMP založené na VR s důrazem na na MDR (medical device regulation) certifikovaný VR rehabilitační zdravotnický prostředek, který byl vyvinut ve spolupráci FN Ostrava a společnosti VR Life.

Stroke is one of the most common causes of acquired disability. It represents a major socio-economic problem that can have a serious impact on different areas of life. Early and sufficiently intensive rehabilitation after stroke contributes significantly to optimal functional outcomes and improves the quality of life of the patients. New neurorehabilitation approaches based on technology and virtual reality (VR) make it possible to design individualized intensive rehabilitation training and improve motor learning through multimodal feedback. Rehabilitation in VR is a highly motivating therapy with many benefits for the patient. It also increases patient compliance to therapy. It is a safe form of therapy and after proper education of the patient, the physical presence of a physiotherapist is not necessarily required. This makes the use of VR in home exercise and telerehabilitation possible. The aim of this review article is to provide up-to-date knowledge and brief information on VR-based neurorehabilitation after stroke, with emphasis on the medical device regulation (MDR)-certified VR interface, which was developed in collaboration between the University Hospital Ostrava and VR Life.

• MeSH
• činnosti denního života MeSH
• klinická studie jako téma metody   MeSH
• kvalita života MeSH
• lidé MeSH
• neurorehabilitace metody   MeSH
• rehabilitace po cévní mozkové příhodě * metody   MeSH
• virtuální realita * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• práce podpořená grantem MeSH

BACKGROUND: Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. METHODS: Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. FINDINGS: Machine learning models achieved a ROC-AUC of 0⋅71 ± 0⋅01, an AUC-PR of 0⋅26 ± 0⋅02, a Brier score of 0⋅1 ± 0⋅01 and an expected calibration error of 0⋅07 ± 0⋅04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. CONCLUSIONS: Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.

• Publikační typ
• časopisecké články MeSH