• Publikační typ
• úvodníky MeSH

This in vitro study aimed to compare outcomes of dental caries detection using visual inspection classified according to the International Caries Detection and Assessment System (ICDAS) with objective assessments using a well-established laser fluorescence system (Diagnodent pen) and a novel diffuse reflectance spectroscopy (DRS) device. One hundred extracted permanent premolars and molars were utilized, including sound teeth, teeth with non-cavitated caries, or teeth with small cavitated lesions. A total of 300 regions of interest (ROIs) were assessed using each detection method. Visual inspection, being a subjective method, was performed by two independent examiners. The presence and extent of caries were histologically verified according to Downer's criteria, serving as a reference for other detection methods. Histological results revealed 180 sound ROIs and 120 carious ROIs, categorized into three different extents of caries. Overall, there was no significant difference between the detection methods in sensitivity (0.90-0.93) and false negative rate (0.05-0.07). However, DRS exhibited superior performance in specificity (0.98), accuracy (0.95), and false positive rate (0.04) compared to other detection methods. Although the tested DRS prototype device exhibited limited penetration depth, it shows promise as a method, particularly for the detection of incipient caries.

• Publikační typ
• časopisecké články MeSH

Forensic dental identification has employed traditionally 2D digital radiological imaging techniques. More recently, 3D cone beam computer tomography (CBCT) data, widely applied in clinical dentistry, have been gradually used. The purpose of this study was to compare the precision and quality of 2D digital orthopantomogram (OPG) and 2D OPG images generated from cone beam computed tomography (CBCT). The study sample consisted of 50 patients with archived conventional 2D OPG and 3D CBCT images. Patients signed an informed consent form to take part in our study. Measurements of the mandible, teeth and dental restorations were taken by two observers on calibrated 2D OPG and 3D CBCT-to-OPG images using measurement functionalities of DOPLHIN software. Acquired dimensions were compared side by side and images of fillings were superimposed. For better visual comparison and more efficient image registration, the methods of spline interpolation were used. The pairs of absolute measurements obtained from conventional OPG and CBCT-to-OPG-converted images were highly correlated (p < 0.05). However, larger, and horizontally measured distances were revealed to be more affected than shorter vertically taken measurements. In relative terms, CBCT-generated width/length indices of the canines and the first molars ranged from 84% to 99.8% of those acquired from traditional OPGs. In addition, corresponding points on the teeth and fillings were compared side by side and in superimposition. The average coincidence of images was 6.1%. The results revealed that for selected metric variables 2D OPGs and 3D CBCT-generated OPGs were complementary and could be used for forensic comparisons.

• MeSH
• digitální zubní radiografie metody   MeSH
• lidé MeSH
• mandibula MeSH
• počítačová tomografie s kuželovým svazkem metody   MeSH
• rentgendiagnostika panoramatická metody   MeSH
• senzitivita a specificita MeSH
• soudní stomatologie * metody   MeSH
• trvalá zubní náhrada MeSH
• zuby MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• srovnávací studie MeSH

Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding.

• MeSH
• ataxie diagnóza   MeSH
• chůze (způsob) MeSH
• kvalita života * MeSH
• lidé MeSH
• neurologické poruchy chůze * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary care. Artificial neural networks (ANNs) are suitable to design computed aided diagnostic systems because of their features of generating relationships between variables and their learning capability. The main aim pursued in that work is to explore the ability of a hybrid ANN-based system in order to provide a tool to assist in the clinical decision-making that facilitates a reliable MCI estimate. The model is designed to work with variables usually available in primary care, including Minimental Status Examination (MMSE), Functional Assessment Questionnaire (FAQ), Geriatric Depression Scale (GDS), age, and years of education. It will be useful in any clinical setting. Other important goal of our study is to compare the diagnostic rendering of ANN-based system and clinical physicians. A sample of 128 MCI subjects and 203 controls was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The ANN-based system found the optimal variable combination, being AUC, sensitivity, specificity, and clinical utility index (CUI) calculated. The ANN results were compared with those from medical experts which include two family physicians, a neurologist, and a geriatrician. The optimal ANN model reached an AUC of 95.2%, with a sensitivity of 90.0% and a specificity of 84.78% and was based on MMSE, FAQ, and age inputs. As a whole, physician performance achieved a sensitivity of 46.66% and a specificity of 91.3%. CUIs were also better for the ANN model. The proposed ANN system reaches excellent diagnostic accuracy although it is based only on common clinical tests. These results suggest that the system is especially suitable for primary care implementation, aiding physicians work with cognitive impairment suspicions.

• MeSH
• databáze faktografické statistika a číselné údaje   MeSH
• diagnóza počítačová metody   statistika a číselné údaje   MeSH
• kognitivní dysfunkce diagnóza   psychologie   MeSH
• lidé MeSH
• neuronové sítě * MeSH
• neuropsychologické testy * statistika a číselné údaje   MeSH
• plocha pod křivkou MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• studie případů a kontrol MeSH
• systémy pro podporu klinického rozhodování * statistika a číselné údaje   MeSH
• výpočetní biologie MeSH
• Check Tag
• lidé MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH

Co může otec udělat pro to, aby jeho dva synové vykročili do života tou správnou nohou? Inženýr Spáčil má jasno. Nachystá pro ně náročnou zkoušku odolnosti v odlehlé horské chalupě. Ovšem navzdory jeho smělému plánu není nouze o komické situace a při čtení se proto skvěle pobavíte!

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.

• MeSH
• algoritmy MeSH
• biosenzitivní techniky * MeSH
• deep learning * MeSH
• dýchání MeSH
• elektrokardiografie MeSH
• entropie MeSH
• lidé MeSH
• obstrukční spánková apnoe MeSH
• počítačové zpracování signálu * MeSH
• polysomnografie MeSH
• poruchy spánku a bdění * MeSH
• srdeční frekvence MeSH
• vlnková analýza MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Due to known information processing capabilities of the brain, neurons are modeled at many different levels. Circuit theory is also often used to describe the function of neurons, especially in complex multi-compartment models, but when used for simple models, there is no subsequent biological justification of used parts. We propose a new single-compartment model of excitatory and inhibitory neuron, the capacitor-switch model of excitatory and inhibitory neuron, as an extension of the existing integrate-and-fire model, preserving the signal properties of more complex multi-compartment models. The correspondence to existing structures in the neuronal cell is then discussed for each part of the model. We demonstrate that a few such inter-connected model units are capable of acting as a chaotic oscillator dependent on fire patterns of the input signal providing a complex deterministic and specific response through the output signal. The well-known necessary conditions for constructing a chaotic oscillator are met for our presented model. The capacitor-switch model provides a biologically-plausible concept of chaotic oscillator based on neuronal cells.

• MeSH
• akční potenciály fyziologie   MeSH
• modely neurologické MeSH
• mozek metabolismus   MeSH
• neurony metabolismus   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈 3 , 8 〉 and 〈 8 , 15 〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.

• MeSH
• akcelerometrie metody   MeSH
• algoritmy MeSH
• Bayesova věta MeSH
• cvičení MeSH
• cyklistika * MeSH
• fitness náramky * MeSH
• lidé MeSH
• mobilní telefon přístrojové vybavení   MeSH
• neuronové sítě MeSH
• počítačové zpracování signálu MeSH
• pohyb těles MeSH
• reprodukovatelnost výsledků MeSH
• rozpoznávání automatizované MeSH
• software MeSH
• srdeční frekvence * MeSH
• statistické modely MeSH
• support vector machine MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep [d=R2]apneaapnoa events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20-35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep [d=R2]apneaapnoas by a sleep specialist. The resulting classifier can mark all [d=R2]apneaapnoa events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. [d=R2]When compared to the classification of polysomnographic breathing signal segments by a sleep specialistand, which is used for calculating length of the event, the classifier has an [d=R1] F 1 score of 92.2%Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep [d=R2]apneaapnoa events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.

• MeSH
• dechová frekvence fyziologie   MeSH
• dospělí MeSH
• dýchání MeSH
• lidé středního věku MeSH
• lidé MeSH
• počítačové zpracování signálu MeSH
• polysomnografie metody   MeSH
• senzitivita a specificita MeSH
• spánek fyziologie   MeSH
• syndromy spánkové apnoe patofyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH