OBJECTIVE: When considering all patients with focal drug-resistant epilepsy, as high as 40-50% of patients suffer seizure recurrence after surgery. To achieve seizure freedom without side effects, accurate localization of the epileptogenic tissue is crucial before its resection. We investigate an automated, fast, objective mapping process that uses only interictal data. METHODS: We propose a novel approach based on multiple iEEG features, which are used to train a support vector machine (SVM) model for classification of iEEG electrodes as normal or pathologic using 30 min of inter-ictal recording. RESULTS: The tissue under the iEEG electrodes, classified as epileptogenic, was removed in 17/18 excellent outcome patients and was not entirely resected in 8/10 poor outcome patients. The overall best result was achieved in a subset of 9 excellent outcome patients with the area under the receiver operating curve = 0.95. CONCLUSION: SVM models combining multiple iEEG features show better performance than algorithms using a single iEEG marker. Multiple iEEG and connectivity features in presurgical evaluation could improve epileptogenic tissue localization, which may improve surgical outcome and minimize risk of side effects. SIGNIFICANCE: In this study, promising results were achieved in localization of epileptogenic regions by SVM models that combine multiple features from 30 min of inter-ictal iEEG recordings.
- MeSH
- Adult MeSH
- Electroencephalography instrumentation methods MeSH
- Epilepsies, Partial diagnosis physiopathology MeSH
- Electrodes, Implanted MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Retrospective Studies MeSH
- Aged MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
OBJECTIVE: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal quality, small number of leads and short records, leading to incorrect recognition of pathological events. This paper introduces a novel approach to classification (normal/'N', atrial fibrillation/'A', other/'O', and noisy/'P') of short single-lead ECGs recorded by wearable devices. APPROACH: Various rhythm and morphology features are derived from the separate beats ('local' features) as well as the entire ECGs ('global' features) to represent short-term events and general trends respectively. Various types of atrial and ventricular activity, heart beats and, finally, ECG records are then recognised by a multi-level approach combining a support vector machine (SVM), decision tree and threshold-based rules. MAIN RESULTS: The proposed features are suitable for the recognition of 'A'. The method is robust due to the noise estimation involved. A combination of radial and linear SVMs ensures both high predictive performance and effective generalisation. Cost-sensitive learning, genetic algorithm feature selection and thresholding improve overall performance. The generalisation ability and reliability of this approach are high, as verified by cross-validation on a training set and by blind testing, with only a slight decrease of overall F1-measure, from 0.84 on training to 0.81 on the tested dataset. 'O' recognition seems to be the most difficult (test F1-measures: 0.90/'N', 0.81/'A' and 0.72/'O') due to high inter-patient variability and similarity with 'N'. SIGNIFICANCE: These study results contribute to multidisciplinary areas, focusing on creation of robust and reliable cardiac monitoring systems in order to improve diagnosis, reduce unnecessary time-consuming expert ECG scoring and, consequently, ensure timely and effective treatment.
- MeSH
- Electrocardiography instrumentation methods MeSH
- Atrial Fibrillation diagnosis MeSH
- Humans MeSH
- Wearable Electronic Devices * MeSH
- Reproducibility of Results MeSH
- Decision Trees MeSH
- Support Vector Machine * MeSH
- Heart Rate Determination instrumentation methods MeSH
- Multilevel Analysis MeSH
- Wavelet Analysis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Research Support, Non-U.S. Gov't MeSH
Objectives: The goals of this study were to examine relationships among health literacy and outcomes for sub-populations identified within a large, multi-dimensional Omaha System dataset. Specific aims were to extract sub-populations from the data using Latent Class Analysis (LCA); and quantify the change in knowledge score from pre- to post-intervention for common sub-populations. Design: Data-driven retrospective study using statistical modeling methods. Sample: A set of admission and discharge cases, captured in the Omaha System, representing 65,468 cases from various health care providers. Measures: Demographic information and the Omaha System terms including problems, signs/symptoms, and interventions were used as the features describing cases used for this study. Development of a mapping of demographics across health care systems enabled the integration of data from these different systems. Results: Knowledge scores increased for all five sub-populations identified by latent class analysis. Effect sizes of interventions related to health literacy outcomes varied from low to high, with the greatest effect size in populations of young at-risk adults. The most significant knowledge gains were seen for problems including Pregnancy, Postpartum, Family planning, Mental health, and Substance use. Conclusions: This is the first study to demonstrate positive relationships between interventions and health literacy outcomes for a very large sample. A deeper analysis of the results, focusing on specific problems and relevant interventions and their impact on health literacy is required to guide resource allocation in community-based care. As such, future work will focus on determining correlations between interventions for specific problems and knowledge change post-intervention.
3rd ed. 223 s. : il.
- MeSH
- Diagnostic Imaging MeSH
- Tomography, X-Ray MeSH
- Tomography MeSH
- Publication type
- Monograph MeSH
- Handbook MeSH
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- radiologie, nukleární medicína a zobrazovací metody
BACKGROUND AND OBJECTIVES: Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Changes in blood cells are caused by malaria disease, and early diagnosis can prevent the disease from entering its severe stage. METHODS: In this paper, an automated non-invasive and efficient deep learning-based framework is developed for multi-class plasmodium vivax life cycle classification and malaria diagnosis. A multi-class microscopic blood cell of different plasmodium vivax life cycle stage dataset is analysed, and a diagnostic framework is designed. Several stages of the disease are examined and augmented through various techniques to make the framework robust in real-time. Generative adversarial network is specially designed to generate extended training samples of various life cycle stages to increase robustness of the resulting model. A special transformer-based neural network vision transformer is designed to improve generalisation capabilities. Microscopic images are classified into multi classes of plasmodium vivax life cycle stage, where the keystone transformer layers extract relevant disease features from microscopic colour images, and the extracted relevant features are used to make predictive diagnostic decisions. RESULTS: The capabilities of the vision transformer are computed and analysed by statistical parameters, and the performance of the vision transformer model is compared with baseline architectures, where it was evident that the performance of the vision transformer was significantly better, reaching 90.03% accuracy. CONCLUSIONS: A comprehensive comparison of the proposed framework to the state-of-the-art methods proves its efficiency in the classification of plasmodium vivax life cycle for malaria disease identification through thin blood smear microscopic images.
- MeSH
- Histological Techniques MeSH
- Malaria * MeSH
- Plasmodium vivax * MeSH
- Life Cycle Stages MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
Objectives: The goals of this study were to examine relationships among health literacy and outcomes for sub-populations identified within a large, multi-dimensional Omaha System dataset. Specific aims were to extract sub-populations from the data using Latent Class Analysis (LCA); and quantify the change in knowledge score from pre- to post-intervention for common sub-populations. Design: Data-driven retrospective study using statistical modeling methods. Sample: A set of admission and discharge cases, captured in the Omaha System, representing 65,468 cases from various health care providers. Measures: Demographic information and the Omaha System terms including problems, signs/symptoms, and interventions were used as the features describing cases used for this study. Development of a mapping of demographics across health care systems enabled the integration of data from these different systems. Results: Knowledge scores increased for all five sub-populations identified by latent class analysis. Effect sizes of interventions related to health literacy outcomes varied from low to high, with the greatest effect size in populations of young at-risk adults. The most significant knowledge gains were seen for problems including Pregnancy, Postpartum, Family planning, Mental health, and Substance use. Conclusions: This is the first study to demonstrate positive relationships between interventions and health literacy outcomes for a very large sample. A deeper analysis of the results, focusing on specific problems and relevant interventions and their impact on health literacy is required to guide resource allocation in community-based care. As such, future work will focus on determining correlations between interventions for specific problems and knowledge change post-intervention.
OBJECTIVE: Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies. METHODS: Among these methods, automatic analysis of speech -one of the first damaged skills in AD patients- is a natural and useful low cost tool for diagnosis. RESULTS: In this paper a non-linear multi-task approach based on automatic speech analysis is presented. Three tasks with different language complexity levels are analyzed, and promising results that encourage a deeper assessment are obtained. Automatic classification was carried out by using classic Multilayer Perceptron (MLP) and Deep Learning by means of Convolutional Neural Networks (CNN) (biologically- inspired variants of MLPs) over the tasks with classic linear features, perceptual features, Castiglioni fractal dimension and Multiscale Permutation Entropy. CONCLUSION: Finally, the most relevant features are selected by means of the non-parametric Mann- Whitney U-test.
- MeSH
- Alzheimer Disease diagnosis MeSH
- Early Diagnosis MeSH
- Deep Learning MeSH
- Diagnosis, Computer-Assisted * methods MeSH
- Adult MeSH
- Cognitive Dysfunction diagnosis MeSH
- Cohort Studies MeSH
- Middle Aged MeSH
- Humans MeSH
- Speech Production Measurement MeSH
- Nonlinear Dynamics MeSH
- Neuropsychological Tests MeSH
- Speech * MeSH
- Pattern Recognition, Automated * methods MeSH
- Aged MeSH
- Speech Recognition Software MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Juxtaglomerular cell tumor (JxGCT) is a rare type of renal neoplasm demonstrating morphologic overlap with some mesenchymal tumors such as glomus tumor (GT) and solitary fibrous tumor (SFT). Its oncogenic drivers remain elusive, and only a few cases have been analyzed with modern molecular techniques. In prior studies, loss of chromosomes 9 and 11 appeared to be recurrent. Recently, whole-genome analysis identified alterations involving genes of MAPK-RAS pathway in a subset, but no major pathogenic alterations have been discovered in prior whole transcriptome analyses. Considering the limited understanding of the molecular features of JxGCTs, we sought to assess a collaborative series with a multiomic approach to further define the molecular characteristics of this entity. Fifteen tumors morphologically compatible with JxGCTs were evaluated using immunohistochemistry for renin, single-nucleotide polymorphism array (SNP), low-pass whole-genome sequencing, and RNA sequencing (fusion assay). In addition, methylation analysis comparing JxGCT, GT, and SFT was performed. All cases tested with renin (n=11) showed positive staining. Multiple chromosomal abnormalities were identified in all cases analyzed (n=8), with gains of chromosomes 1p, 10, 17, and 19 and losses of chromosomes 9, 11, and 21 being recurrent. A pathogenic HRAS mutation was identified in one case as part of the SNP array analysis. Thirteen tumors were analyzed by RNA sequencing, with 2 revealing in-frame gene fusions: TFG::GPR128 (interpreted as stochastic) and NAB2::STAT6 . The latter, originally diagnosed as JxGCT, was reclassified as SFT and excluded from the series. No fusions were detected in the remaining 11 cases; of note, no case harbored NOTCH fusions previously described in GT. Genomic methylation analysis showed that JxGCT, GT, and SFT form separate clusters, confirming that JxGCT represents a distinct entity (ie, different from GT). The results of our study show that JxGCTs are a distinct tumor type with a recurrent pattern of chromosomal imbalances that may play a role in oncogenesis, with MAPK-RAS pathway activation being likely a driver in a relatively small subset.
- MeSH
- Adult MeSH
- Epigenesis, Genetic MeSH
- Epigenomics MeSH
- Gene Fusion * MeSH
- Genetic Predisposition to Disease MeSH
- Genomics MeSH
- Immunohistochemistry MeSH
- Polymorphism, Single Nucleotide MeSH
- Juxtaglomerular Apparatus pathology MeSH
- Middle Aged MeSH
- Humans MeSH
- DNA Methylation MeSH
- Biomarkers, Tumor * genetics MeSH
- Kidney Neoplasms * genetics pathology chemistry MeSH
- Whole Genome Sequencing MeSH
- Aged MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
Závěrečná zpráva o řešení grantu Agentury pro zdravotnický výzkum MZ ČR
Nestr.
Auditivní halucinace (AH) jsou nejtypičtějším symptomem psychóz a zejména schizofrenie a z klinického i fenomenologického hlediska tuto skupinu onemocnění definují. Neuronální mechanismus AH však stále nebyl dostatečně popsán. Podle nejvlivnější teorie, vysvětlující původ auditivní verbálních halucinací, je jejich příčinou odcizení vnitřní řeči, což se vztahuje k narušení sebemonitorování, ještě přesněji pak k narušení prediktivních mechanismů. Dalším možným vysvětlením AH se vztahuje k dialogické a monologické vnitřní řeči. V této studii použijeme multi-modální přístup založený na detailním popisu fenomenologie a testování současných neurálních teoriích AH. Subtypy AH založené na fenomenologickém rozboru a společných neurobiologických mechanismech získaných z fMRI dat, mohou vést k diferencovanější a efektivnější léčbě AH, a zlepšit tak kvalitu života pacientů.; Auditory hallucinations (AHs) are the most characteristic symptom of schizophrenia and psychosis, and they „define“ the disorder from a clinical and phenomenological point of view. To date, neural mechanisms of AHs are under debate. The most accepted theory explaining origin of auditory verbal hallucinations, is theory which explains them as alienated inner-speech relates to deficits in self-monitoring, specifically suggesting impaired usage of predictive mechanisms. Another plausible explanation of this typical feature of AHs relates to dialogical or monological inner speech. In the current study we will use a multi-modal approach based on the detailed description of phenomenology and probing the currents neural theories of AHs. Subtypization of AHs based on phenomenology and common neurobiological mechanisms as obtained by fMRI, would potentially lead to more differentiated and effective treatment of AHs and improve patient’s outcomes.
- Keywords
- fenomenologie,
- MeSH
- Mental Processes MeSH
- Ego MeSH
- Philosophy MeSH
- Hallucinations etiology psychology MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Neurobiology MeSH
- Psychiatric Status Rating Scales MeSH
- Psychometrics methods MeSH
- Schizophrenia diagnosis MeSH
- Self Concept MeSH
- Behavior Rating Scale MeSH
- Check Tag
- Humans MeSH
- Conspectus
- Psychiatrie
- NML Fields
- psychiatrie
- neurologie
- NML Publication type
- závěrečné zprávy o řešení grantu AZV MZ ČR
OBJECTIVE: Age-at-death estimation is usually done manually by experts. As such, manual estimation is subjective and greatly depends on the past experience and proficiency of the expert. This becomes even more critical if experts need to evaluate individuals with unknown population affinity or with affinity that they are not familiar with. The purpose of this study is to design a novel age-at-death estimation method allowing for automatic evaluation on computers, thus eliminating the human factor. METHODS: We used a traditional machine-learning approach with explicit feature extraction. First, we identified and described the features that are relevant for age-at-death estimation. Then, we created a multi-linear regression model combining these features. Finally, we analysed the model performance in terms of Mean Absolute Error (MAE), Mean Bias Error (MBE), Slope of Residuals (SoR) and Root Mean Squared Error (RMSE). RESULTS: The main result of this study is a population-independent method of estimating an individual's age-at-death using the acetabulum of the pelvis. Apart from data acquisition, the whole procedure of pre-processing, feature extraction and age estimation is fully automated and implemented as a computer program. This program is a part of a freely available web-based software tool called CoxAGE3D, which is available at https://coxage3d.fit.cvut.cz/. Based on our dataset, the MAE of the presented method is about 10.7 years. In addition, five population-specific models for Thai, Lithuanian, Portuguese, Greek and Swiss populations are also given. The MAEs for these populations are 9.6, 9.8, 10.8, 10.5 and 9.2 years, respectively. Our age-at-death estimation method is suitable for individuals with unknown population affinity and provides acceptable accuracy. The age estimation error cannot be completely eliminated, because it is a consequence of the variability of the ageing process of different individuals not only across different populations but also within a certain population.
- MeSH
- Acetabulum * diagnostic imaging MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Linear Models MeSH
- Young Adult MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Software * MeSH
- Forensic Anthropology * methods MeSH
- Machine Learning * MeSH
- Age Determination by Skeleton * methods MeSH
- Imaging, Three-Dimensional * MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
