BACKGROUND: The increasing prevalence of mental health disorders among adolescents highlights the importance of early identification and intervention. Artemis-A is a web-based application of computerised adaptive testing (CAT), originally developed for secondary schools, to quickly and efficiently assess students' mental health. Due to its speed, reliability and accessibility, it may be a valuable tool for healthcare practitioners (HCPs) working with children and young people (CYP) in primary, community and potentially secondary care settings in the future. OBJECTIVE: To explore whether Artemis-A would be a useful, feasible and acceptable tool for HCPs working in primary and community care settings to identify CYP's mental health difficulties. METHODS: Semistructured interviews were conducted with 20 HCPs: 5 general practitioners, 5 Child and Adolescent Mental Health Services (CAMHS) staff, 5 school nurses and 5 community paediatricians. Data were analysed using the Framework approach. FINDINGS: HCPs reported that Artemis-A has the potential to enhance mental health assessment and aid overburdened services by providing a quick, patient-centred assessment and monitoring mechanism. Benefits of the app include facilitating earlier intervention and appropriate referrals. However, some concerns emerged about safety netting and the way Artemis-A presents its information. Responsibilities for ensuring care continuity also require careful clarification. CONCLUSIONS: With proper protocols and integration, Artemis-A could prove valuable in supporting HCPs to promptly detect mental health issues in CYP. Further research into optimal implementation is warranted. CLINICAL IMPLICATIONS: If paired with effective evidence-based interventions, the implementation of Artemis-A could help manage escalating demands in CAMHS.

• Klíčová slova
• Child & adolescent psychiatry,
• MeSH
• diagnóza počítačová * metody   MeSH
• dítě MeSH
• dospělí MeSH
• duševní poruchy * diagnóza   MeSH
• kvalitativní výzkum MeSH
• lidé MeSH
• mladiství MeSH
• primární zdravotní péče MeSH
• služby péče o duševní zdraví MeSH
• studie proveditelnosti MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Spojené království MeSH

To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min-max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min-max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.

• Klíčová slova
• Ant Colony Optimisation, Bayesian optimisation, Cardiovascular disease, Hyperparameter, Min–max scaler,
• MeSH
• Bayesova věta MeSH
• deep learning * MeSH
• diagnóza počítačová metody   MeSH
• Formicidae MeSH
• kardiovaskulární nemoci * diagnóza   MeSH
• lidé MeSH
• neuronové sítě * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Large-scale biorepositories and databases are essential to generate equitable, effective, and sustainable advances in cancer prevention, early detection, cancer therapy, cancer care, and surveillance. The Mutographs project has created a large genomic dataset and biorepository of over 7,800 cancer cases from 30 countries across five continents with extensive demographic, lifestyle, environmental, and clinical information. Whole-genome sequencing is being finalized for over 4,000 cases, with the primary goal of understanding the causes of cancer at eight anatomic sites. Genomic, exposure, and clinical data will be publicly available through the International Cancer Genome Consortium Accelerating Research in Genomic Oncology platform. The Mutographs sample and metadata biorepository constitutes a legacy resource for new projects and collaborations aiming to increase our current research efforts in cancer genomic epidemiology globally.

• MeSH
• banky biologického materiálu MeSH
• databáze faktografické MeSH
• genomika MeSH
• lidé MeSH
• nádory * diagnóza   MeSH
• poskytování zdravotní péče MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

BACKGROUND AND OBJECTIVES: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems. METHODS: The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network. RESULTS: Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset. CONCLUSIONS: Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.

• Klíčová slova
• CAD, CNN, EEG, Seizures,
• MeSH
• diagnóza počítačová MeSH
• elektroencefalografie metody   MeSH
• lidé MeSH
• neuronové sítě * MeSH
• počítačové zpracování signálu MeSH
• systémová analýza MeSH
• záchvaty * diagnostické zobrazování   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Glomus jugulare tumours (GJT) are benign tumours that arise locally and destructively in the base of the skull and can be successfully treated with radiotherapy. Patients have a long-life expectancy and the late effects of radiotherapy can be serious. Proton radiotherapy reduces doses to critical organs and can reduce late side effects of radiotherapy. The aim of this study was to report feasibility and early clinical results of 12 patients treated using proton therapy. METHODS: Between December 2013 and June 2019, 12 patients (pts) with GJT (median volume 20.4 cm3 ; range 8.5-41 cm3 ) were treated with intensity modulated proton therapy (IMPT). Median dose was 54 GyE (Gray Equivalents) (50-60 GyE) with daily fractions of 2 GyE. Twelve patients were analysed with a median follow-up time of 42.2 months (11.3-86.7). Feasibility, dosimetric parameters, acute and late toxicity and local effect on tumour were evaluated in this retrospective study. RESULTS: All patients finished treatment without interruption, with excellent dosimetric parameters and mild acute toxicity. Stabilisation of tumour size was detected on MRI in all patients. No changes in symptoms were observed in comparison with pre-treatment conditions. No late effects of radiotherapy were observed. CONCLUSION: Pencil-beam scanning proton radiotherapy is highly feasible in the treatment of large GJT with mild acute toxicity and promising short-term results. Longer follow-up and larger patient cohorts are required to further identify the role of pencil-beam scanning (PBS) for this indication.

• Klíčová slova
• Head and neck, glomus jugulare, proton radiotherapy, radiation oncology,
• MeSH
• celková dávka radioterapie MeSH
• lidé MeSH
• plánování radioterapie pomocí počítače metody   MeSH
• protonová terapie * škodlivé účinky   metody   MeSH
• protony MeSH
• radioterapie s modulovanou intenzitou * škodlivé účinky   metody   MeSH
• retrospektivní studie MeSH
• tumor glomus jugulare * etiologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• protony MeSH

Objective: Vectorcardiography (VCG) as an alternative form of ECG provides important spatial information about the electrical activity of the heart. It achieves higher sensitivity in the detection of some pathologies such as myocardial infarction, ischemia and hypertrophy. However, vectorcardiography is not commonly measured in clinical practice, and for this reason mathematical transformations have been developed to obtain derived VCG leads, which in application in current systems and subsequent analysis can contribute to early diagnosis and obtaining other useful information about the electrical activity of the heart. Methods and procedures: The most frequently used transformation methods are compared, namely the Kors regression method, the Inverse Dower transformation, QLSV and the Quasi-orthogonal transformation. These transformation methods were used on 30 randomly selected records with a diagnosis of myocardial infarction from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated based on the calculation of the mean square error (MSE). MSE was subjected to statistical evaluation at a significance level of 0.05. Results: Based on statistical testing using the nonparametric multiselective Kruskall-Wallis test and subsequent post-hoc analysis using the Dunn method, the Kors regression as a whole method achieved the most accurate transformation. Conclusion: The results of statistical analysis provide an evaluation of the accuracy of several transformation methods for deriving orthogonal leads, for possible application in measuring and evaluation systems, which may contribute to the correct choice of method for subsequent analysis of electrical activity of the heart at orthogonal leads to predict various diseases.

• Klíčová slova
• Transformation methods, statistical evaluation, transformation matrix, vectorcardiography,
• MeSH
• databáze faktografické MeSH
• diagnóza počítačová * metody   MeSH
• infarkt myokardu * diagnóza   MeSH
• lidé MeSH
• srdce MeSH
• vektorkardiografie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.

• Klíčová slova
• HIDS, IoMT, IoT, NIDS, NetFlow data, fog computing, hybrid attack detection, incremental learning, machine learning, sensor’s data,
• MeSH
• Bayesova věta MeSH
• big data MeSH
• časná diagnóza MeSH
• internet věcí * MeSH
• Publikační typ
• časopisecké články MeSH

PURPOSE: The aim of the study was to compare the assessment of ischemic changes by expert reading and available automated software for non-contrast CT (NCCT) and CT perfusion on baseline multimodal imaging and demonstrate the accuracy for the final infarct prediction. METHODS: Early ischemic changes were measured by ASPECTS on the baseline neuroimaging of consecutive patients with anterior circulation ischemic stroke. The presence of early ischemic changes was assessed a) on NCCT by two experienced raters, b) on NCCT by e-ASPECTS, and c) visually on derived CT perfusion maps (CBF<30%, Tmax>10s). Accuracy was calculated by comparing presence of final ischemic changes on 24-hour follow-up for each ASPECTS region and expressed as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The subanalysis for patients with successful recanalization was conducted. RESULTS: Of 263 patients, 81 fulfilled inclusion criteria. Median baseline ASPECTS was 9 for all tested modalities. Accuracy was 0.76 for e-ASPECTS, 0.79 for consensus, 0.82 for CBF<30%, 0.80 for Tmax>10s. e-ASPECTS, consensus, CBF<30%, and Tmax>10s had sensitivity 0.41, 0.46, 0.49, 0.57, respectively; specificity 0.91, 0.93, 0.95, 0.91, respectively; PPV 0.66, 0.75, 0.82, 0.73, respectively; NPV 0.78, 0.80, 0.82, 0.83, respectively. Results did not differ in patients with and without successful recanalization. CONCLUSION: This study demonstrated high accuracy for the assessment of ischemic changes by different CT modalities with the best accuracy for CBF<30% and Tmax>10s. The use of automated software has a potential to improve the detection of ischemic changes.

• Klíčová slova
• ASPECTS, CT perfusion, Early ischemic changes, RAPID, Stroke imaging, e-ASPECTS,
• MeSH
• časná diagnóza MeSH
• časové faktory MeSH
• cévní mozková příhoda diagnostické zobrazování   patofyziologie   terapie   MeSH
• dospělí MeSH
• ischemie mozku diagnostické zobrazování   patofyziologie   terapie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mozkový krevní oběh * MeSH
• perfuzní zobrazování metody   MeSH
• počítačová rentgenová tomografie * MeSH
• prediktivní hodnota testů MeSH
• prognóza MeSH
• rentgenový obraz - interpretace počítačová * MeSH
• reprodukovatelnost výsledků MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• software * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• pozorovací studie MeSH
• srovnávací studie MeSH

Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed.Objectives: To develop and validate a deep learning method to improve the management of IPNs.Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions.Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4-90.7%) and 91.9% (95% CI, 88.7-94.7%), compared with 78.1% (95% CI, 68.7-86.4%) and 81.9 (95% CI, 76.1-87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts.Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.

• Klíčová slova
• computer-aided image analysis, early detection, lung cancer, neural networks, risk stratification,
• MeSH
• algoritmy MeSH
• deep learning * MeSH
• lidé MeSH
• mnohočetné plicní uzly diagnostické zobrazování   MeSH
• nádory plic diagnostické zobrazování   epidemiologie   patofyziologie   MeSH
• neuronové sítě MeSH
• počítačová rentgenová tomografie metody   MeSH
• rentgenový obraz - interpretace počítačová metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Geografické názvy
• Spojené státy americké epidemiologie   MeSH

BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI algorithm, the lung cancer prediction convolutional neural network (LCP-CNN), with that of the Brock University model, recommended in UK guidelines. METHODS: A dataset of incidentally detected pulmonary nodules measuring 5-15 mm was collected retrospectively from three UK hospitals for use in a validation study. Ground truth diagnosis for each nodule was based on histology (required for any cancer), resolution, stability or (for pulmonary lymph nodes only) expert opinion. There were 1397 nodules in 1187 patients, of which 234 nodules in 229 (19.3%) patients were cancer. Model discrimination and performance statistics at predefined score thresholds were compared between the Brock model and the LCP-CNN. RESULTS: The area under the curve for LCP-CNN was 89.6% (95% CI 87.6 to 91.5), compared with 86.8% (95% CI 84.3 to 89.1) for the Brock model (p≤0.005). Using the LCP-CNN, we found that 24.5% of nodules scored below the lowest cancer nodule score, compared with 10.9% using the Brock score. Using the predefined thresholds, we found that the LCP-CNN gave one false negative (0.4% of cancers), whereas the Brock model gave six (2.5%), while specificity statistics were similar between the two models. CONCLUSION: The LCP-CNN score has better discrimination and allows a larger proportion of benign nodules to be identified without missing cancers than the Brock model. This has the potential to substantially reduce the proportion of surveillance CT scans required and thus save significant resources.

• Klíčová slova
• CT imaging, lung cancer, non-small cell lung cancer,
• MeSH
• algoritmy MeSH
• časná detekce nádoru metody   MeSH
• databáze faktografické MeSH
• dospělí MeSH
• hodnocení rizik MeSH
• incidence MeSH
• invazivní růst nádoru patologie   MeSH
• kohortové studie MeSH
• lidé středního věku MeSH
• lidé MeSH
• mnohočetné plicní uzly epidemiologie   patologie   patofyziologie   MeSH
• nádorová transformace buněk patologie   MeSH
• nádory plic epidemiologie   patologie   patofyziologie   MeSH
• neuronové sítě * MeSH
• plocha pod křivkou MeSH
• prediktivní hodnota testů MeSH
• prognóza MeSH
• retrospektivní studie MeSH
• ROC křivka MeSH
• senioři MeSH
• staging nádorů MeSH
• umělá inteligence * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH
• validační studie MeSH