BACKGROUND: 3D printing is one of the fastest-growing technologies in medicine, but it is essential to have a system for 3D printing documentation that is accessible for not only clinical engineers and surgeons, but also quality managers and data-privacy officers in hospitals. Dedicated software such as product lifecycle management (PLM) software could enable comprehensive management and traceability of all data relevant to 3D printing tasks in a hospital and would highly beneficial. Therefore, customizable software called 3Diamond was developed for 3D printing in medicine. METHODS: The software development process involved several stages, including setting specifications based on end-user requirements, design, implementation, and testing. In order to ensure the software's long-term success and smooth operation, critical phases were also considered, such as deployment and maintenance. RESULTS: The developed software provides immediate and complete traceability of all preparations and controls, as well as management of reports, orders, stock, and post-operative follow-up of tasks related to 3D printing in a hospital. Based on user requirements, software testing is provided automatically with each release. The software was implemented in a natural clinical environment with a developed 3D printing center. CONCLUSION: Although 3D printing has potential for innovation in the medical profession, it is nevertheless subject to regulations. Even though there are exemptions for patient-specific products, the effects of their local legal implementations related to 3D printing cannot be fully overseen. To this end, 3Diamond provides a robust system for 3D printing documentation that is accessible to different personnel in hospitals.

• Publikační typ
• časopisecké články MeSH

Zaměřujeme se na možné využití AI v rámci diagnostiky ložiskových změn plicního parenchymu, které mohou být projevem zhoubného nádoru plic, na základě skiagramu hrudníku. Ačkoliv ve srovnání s jinými metodami, především výpočetní tomografií (CT) hrudníku, tato modalita vykazuje nižší senzitivitu, vzhledem k rutinnímu provádění velmi často představuje první vyšetření, při němž jsou plicní léze zachyceny. Prezentujeme vlastní řešení založené na metodách hlubokého učení, které má za cíl zvýšit záchyt plicních lézí především v časných fázích onemocnění. Následně uvádíme výsledky našich předchozích původních prací, které validují navržený model ve dvou odlišných klinických prostředích – v prostředí spádové nemocnice s nízkou prevalencí nálezů a v prostředí specializovaného onkologického centra. Na základě kvantitativního srovnání se závěry radiologů různých úrovní zkušeností jsme zjistili, že náš model dosahuje vysoké senzitivity, na druhou stranu byla jeho specificita nižší než u oslovených radiologů. V kontextu klinických požadavků a diagnostiky asistované AI hraje zásadní roli zkušenost a klinické uvažování lékaře, proto se v současnosti přikláníme k modelům s vyšší senzitivitou na úkor nižší specificity. V případě suspekce, byť vyhodnocené jako nepravděpodobné, model nález raději předkládá lékaři. Na základě těchto výsledků lze očekávat, že v budoucnu bude AI hrát klíčovou roli v oblasti radiologie jako pomocný nástroj pro hodnotící specialisty. Aby k tomu mohlo dojít, je potřeba vyřešit nejen technické, ale i některé medicínské a regulatorní aspekty. Zásadní je dostupnost kvalitních a spolehlivých informací nejen o přínosech, ale také o limitacích možností strojového učení a AI v medicíně.

In recent years healthcare is undergoing significant changes due to technological innovations, with Artificial Intelligence (AI) being a key trend. Particularly in radiodiagnostics, according to studies, AI has the potential to enhance accuracy and efficiency. We focus on AI’s role in diagnosing pulmonary lesions, which could indicate lung cancer, based on chest X-rays. Despite lower sensitivity in comparison to other methods like chest CT, due to its routine use, X-rays often provide the first detection of lung lesions. We present our deep learning-based solution aimed at improving lung lesion detection, especially during early-stage of illness. We then share results from our previous studies validating this model in two different clinical settings: a general hospital with low prevalence findings and a specialized oncology center. Based on a quantitative comparison with the conclusions of radiologists of different levels of experience, our model achieves high sensitivity, but lower specificity than comparing radiologists. In the context of clinical requirements and AI-assisted diagnostics, the experience and clinical reasoning of the doctor play a crucial role, therefore we currently lean more towards models with higher sensitivity over specificity. Even unlikely suspicions are presented to the doctor. Based on these results, it can be expected that in the future artificial intelligence will play a key role in the field of radiology as a supporting tool for evaluating specialists. To achieve this, it is necessary to solve not only technical but also medical and regulatory aspects. It is crucial to have access to quality and reliable information not only about the benefits but also about the limitations of machine learning and AI in medicine.

• Klíčová slova
• skiagram hrudníku,
• MeSH
• časná detekce nádoru metody   MeSH
• hrudník * diagnostické zobrazování   MeSH
• interpretace obrazu počítačem MeSH
• lidé MeSH
• nádory plic diagnostické zobrazování   MeSH
• radiografie MeSH
• retrospektivní studie MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Geografické názvy
• Česká republika MeSH

The recent technological advances enabled the biomedical research to explore the underlying biological processes of the living organisms at various resolutions and from different perspectives. While the amount of produced data grew dramatically over the years, the pace at which our knowledge lagged behind - an indication of inability of the current computational tools to extract knowledge from the large pool of noisy data. NEUROMINER will provide a framework for machine learning and data mining, with a special emphasis on neuroscience research. The project has three main axes of research, each corresponding to a currently unmet need: (1) extraction and selection of features with strong discrimination properties, (2) systems able to learn from high-dimensional data and not suffering from overfitting problems, and (3) rigorous statistical model assessment procedure. The applicants are experts in medical image processing and analysis, biostatistics and machine learning.

Nedávné technologické pokroky biomedicínského výzkumu umožnily zkoumat základní biologické procesy v živých organismech při různých rozlišeních a z různých úhlů pohledu. Zatímco množství produkovaných dat v průběhu let dramaticky roste, tempo našich získávaných znalostí spíše zaostává, což ukazuje na neschopnost současných výpočetních nástrojů umožnit extrakci znalostí z velkého množství zašuměných dat. NEUROMINER poskytne rámec pro strojové učení a dolování z obrazových dat se zvláštním důrazem na neurovědní výzkum. Tři hlavní osy projektu odpovídají problémům, pro které v současné době není známo řešení: (1) extrakce a selekce příznaků se silnou diskriminačních schopností z mnohorozměrných dat, (2) nepřeučené systémy učící se z mnohorozměrných dat (3) rigorózní postup pro statistické validace modelů. Navrhovatelé projektu jsou experty ve zpracování analýze medicínských obrazů, biostatistice a strojovém učení.

• MeSH
• biostatistika MeSH
• data mining MeSH
• mozek diagnostické zobrazování   MeSH
• neuronové sítě MeSH
• neurozobrazování MeSH
• počítačové zpracování obrazu MeSH
• reprodukovatelnost výsledků MeSH
• schizofrenie diagnostické zobrazování   MeSH
• strojové učení MeSH

• Konspekt
• Patologie. Klinická medicína
• NLK Obory
• neurologie
• radiologie, nukleární medicína a zobrazovací metody
• lékařská informatika
• NLK Publikační typ
• závěrečné zprávy o řešení grantu AZV MZ ČR

Schizophrenia is a severe neuropsychiatric disease whose diagnosis, unfortunately, lacks an objective diagnostic tool supporting a thorough psychiatric examination of the patient. We took advantage of today's computational abilities, structural magnetic resonance imaging, and modern machine learning methods, such as stacked autoencoders (SAE) and 3D convolutional neural networks (3D CNN), to teach them to classify 52 patients with schizophrenia and 52 healthy controls. The main aim of this study was to explore whether complex feature extraction methods can help improve the accuracy of deep learning-based classifiers compared to minimally preprocessed data. Our experiments employed three commonly used preprocessing steps to extract three different feature types. They included voxel-based morphometry, deformation-based morphometry, and simple spatial normalization of brain tissue. In addition to classifier models, features and their combination, other model parameters such as network depth, number of neurons, number of convolutional filters, and input data size were also investigated. Autoencoders were trained on feature pools of 1000 and 5000 voxels selected by Mann-Whitney tests, and 3D CNNs were trained on whole images. The most successful model architecture (autoencoders) achieved the highest average accuracy of 69.62% (sensitivity 68.85%, specificity 70.38%). The results of all experiments were statistically compared (the Mann-Whitney test). In conclusion, SAE outperformed 3D CNN, while preprocessing using VBM helped SAE improve the results.

• Publikační typ
• časopisecké články MeSH

Pathological changes in the cortical lamina can cause several mental disorders Visualization of these changes in vivo would enhance their diagnostics Recently a framework for visualizing cortical structures by magnetic resonance imaging MRI has emerged This is based on mathematical modeling of multi component T1 relaxation at the sub voxel level This work proposes a new approach for thei

• Publikační typ
• časopisecké články MeSH

Background: The research of primary progressive multiple sclerosis (PPMS) has not been able to capitalize on recent progresses in advanced magnetic resonance imaging (MRI) protocols. Objective: The presented cross-sectional study evaluated the utility of four different MRI relaxation metrics and diffusion-weighted imaging in PPMS. Methods: Conventional free precession T1 and T2, and rotating frame adiabatic T1ρ and T2ρ in combination with diffusion-weighted parameters were acquired in 13 PPMS patients and 13 age- and sex-matched controls. Results: T1ρ, a marker of crucial relevance for PPMS due to its sensitivity to neuronal loss, revealed large-scale changes in mesiotemporal structures, the sensorimotor cortex, and the cingulate, in combination with diffuse alterations in the white matter and cerebellum. T2ρ, particularly sensitive to local tissue background gradients and thus an indicator of iron accumulation, concurred with similar topography of damage, but of lower extent. Moreover, these adiabatic protocols outperformed both conventional T1 and T2 maps and diffusion tensor/kurtosis approaches, methods previously used in the MRI research of PPMS. Conclusion: This study introduces adiabatic T1ρ and T2ρ as elegant markers confirming large-scale cortical gray matter, cerebellar, and white matter alterations in PPMS invisible to other in vivo biomarkers.

• Publikační typ
• časopisecké články MeSH

BACKGROUND: Statistical analysis, which has become an integral part of evidence-based medicine, relies heavily on data quality that is of critical importance in modern clinical research. Input data are not only at risk of being falsified or fabricated, but also at risk of being mishandled by investigators. OBJECTIVE: The urgent need to assure the highest data quality possible has led to the implementation of various auditing strategies designed to monitor clinical trials and detect errors of different origin that frequently occur in the field. The objective of this study was to describe a machine learning-based algorithm to detect anomalous patterns in data created as a consequence of carelessness, systematic error, or intentionally by entering fabricated values. METHODS: A particular electronic data capture (EDC) system, which is used for data management in clinical registries, is presented including its architecture and data structure. This EDC system features an algorithm based on machine learning designed to detect anomalous patterns in quantitative data. The detection algorithm combines clustering with a series of 7 distance metrics that serve to determine the strength of an anomaly. For the detection process, the thresholds and combinations of the metrics were used and the detection performance was evaluated and validated in the experiments involving simulated anomalous data and real-world data. RESULTS: Five different clinical registries related to neuroscience were presented-all of them running in the given EDC system. Two of the registries were selected for the evaluation experiments and served also to validate the detection performance on an independent data set. The best performing combination of the distance metrics was that of Canberra, Manhattan, and Mahalanobis, whereas Cosine and Chebyshev metrics had been excluded from further analysis due to the lowest performance when used as single distance metric-based classifiers. CONCLUSIONS: The experimental results demonstrate that the algorithm is universal in nature, and as such may be implemented in other EDC systems, and is capable of anomalous data detection with a sensitivity exceeding 85%.

• Publikační typ
• časopisecké články MeSH

This work explores the design and implementation of an algorithm for the classification of magnetic resonance imaging data for computer-aided diagnosis of schizophrenia. Features for classification were first extracted using two morphometric methods: voxel-based morphometry (VBM) and deformation-based morphometry (DBM). These features were then transformed into a wavelet domain using the discrete wavelet transform with various numbers of decomposition levels. The number of features was then reduced by thresholding and subsequent selection by: Fisher's Discrimination Ratio (FDR), Bhattacharyya Distance, and Variances (Var.). A Support Vector Machine with a linear kernel was used for classification. The evaluation strategy was based on leave-one-out cross-validation.

• MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• schizofrenie * MeSH
• support vector machine MeSH
• vlnková analýza MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Cíl studie: Zjistit aktuální anesteziologickou praxi pro výkony v časném poporodním období v České republice (ČR) a na Slovensku (SR). Typ studie: Prospektivní observační mezinárodní. Pracoviště: Sto pět pracovišť v ČR a SR poskytujících anestezii v peripartálním období. Materiál a metoda: Se souhlasem Etické komise jsme oslovili všech 149 pracovišť poskytujících anestezii v peripartálním období v ČR i SR s cílem zachytit všechny případy podání anestezie v peripartálním období v listopadu 2015. Data byla zadávána elektronickým záznamem se dvěma částmi (Demografie 2014 a Podání anestezie) do databáze vytvořené v systému CLADE-IS. Soubor dat byl analyzován deskriptivně s použitím softwaru SPSS24. Výsledky: Do studie bylo zařazeno 105 center (70,5 % v ČR a SR), která reprezentovala 87,7 % ze všech porodů v ČR (7 256 ze 8 275) a 66,4 % v SR (2 863 ze 4 311). Bylo zařazeno 3 523 případů anesteziologické péče v peripartálním období, z čehož v časném poporodním období to bylo 181 (5,1 %): manuální lýza (108, 59,7 %), revize dutiny děložní (61, 33,7 %) a ošetření rozsáhlého porodního poranění (58, 32 %). Anestezie byla podána celková (168, 92,8 %) nebo epidurální (13, 7,2 %) u pacientek s již zavedeným epidurálním katétrem. Zajištění dýchacích cest bylo obličejovou maskou (117, 64,6 %) nebo orotracheální intubací (32, 17,7 %) s bleskovým úvodem s užitím sukcinilcholinu (27, 84,4 %) s aplikací Sellickova hmatu (8, 29,6 %). V úvodu byl použit propofol (147, 81,2 %), ketamin (29, 16,0 %) nebo thiopental (8, 4,4 %). Analgezie byla zajišťována sufentanilem (78, 43 %) nebo alfentanilem (51, 28,2 %). Závěr: V obou zemích převládá užití celkové anestezie u výkonů v časném poporodním období s obličejovou maskou a propofolem.

Objectives: To determine the anaesthesia practice for early postpartum procedures in the Czech Republic (CZE) and Slovakia (SK). Design: International, prospective, observational. Departments: 105 departments providing obstetric anaesthesia in CZE and SK. Methods: With Ethical Committee approval we aimed to enrol all the 149 obstetric departments in CZE and SK and to monitor every case of anaesthetic care in the peripartum period during November 2015. The data were recorded into CLADE-IS database through a Case Report Form consisting of two parts (Demography 2014 and Case Report). The database was analysed descriptively with SPSS24 software. Results: We enrolled 105 centres (70.5%) representing 87.7% of all births in the CZE (7256 out of 8275) and 66.4% in the SK (2863 out of 4311). There were 3523 cases of anaesthetic care in the peripartum period, of which 181 (5.1%) were in the early postpartum period: manual lysis (108, 59.7%), postpartum uterine cavity revision (61, 33.7%) and sewing of extensive birth injuries (58, 32.0%). The mode of anaesthesia was general (168, 92.8%), or epidural (13, 7.2%) in parturients with an epidural catheter in-situ. The airway was secured with face mask (117, 64.6%) or orotracheal intubation (32, 17.7%). Rapid Sequence Induction and succinylcholine (27, 84.4%) with cricoid pressure (8, 29.6%) were used for intubation. The anaesthetic agent used for induction was propofol (147, 81.2%), ketamine (29, 16.0%) or thiopental (8.44%). Analgesia was provided with sufentanil (78, 43.0%) and alfentanil (51, 28.2%). Conclusion: Anaesthetists in the Czech Republic and Slovakia prefer general anaesthesia with face mask and propofol for minor surgery in the postpartum period.

• MeSH
• anestetika MeSH
• anestezie * metody   MeSH
• lidé MeSH
• poporodní období MeSH
• pozorovací studie jako téma MeSH
• prospektivní studie MeSH
• zajištění dýchacích cest metody   MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH
• Slovenská republika MeSH

BACKGROUND: Medical education, in general, is undergoing a significant shift from traditional methods. It becomes very difficult to discover effective teaching methods within the limited possibilities in patient hands-on education, especially as seen in anesthesiology and intensive care medicine (AIM) teaching. Motivation-based teaching is very popular in all other aspects of education, but it has received scant attention in medical education literature, even though it can make a real difference for both students and physicians. OBJECTIVE: The primary aim of this retrospective audit was to find out if proper motivation-based teaching of students via the development of AKUTNE.CZ's serious games can help retain graduates of the Faculty of Medicine of Masaryk University (FMMU) for the AIM specialty. METHODS: Motivation-based teaching and the learning-by-doing concept were applied to a subject called Individual Project. Our topic, The Development of the Multimedia Educational Portal, AKUTNE.CZ, has been offered since 2010. The objective has been the development of supportive material in the form of interactive algorithms, serious games, and virtual patients for problem-based learning or team-based learning lectures aimed at acute medicine. We performed a retrospective questionnaire evaluation of all participants from the 2010-2017 period, focusing on their choice of medical specialty in 2017. The data were reported descriptively. RESULTS: We evaluated 142 students who passed Individual Project with topic The Development of the Multimedia Educational Portal, AKUTNE.CZ during 2010 to 2017. In this period, they developed up to 77 electronic serious games in the form of interactive multimedia algorithms. Out of 139 students in general medicine, 108 students (77.7%) had already graduated and 37 graduates (34.3%) worked in the AIM specialty. Furthermore, 57 graduates (52.8%) chose the same specialty after graduation, matching the topic of their algorithm, and 37 (65%) of these graduates decided to pursue AIM. CONCLUSIONS: Motivation-based teaching and the concept of learning-by-doing by the algorithm/serious game development led to the significant retention of FMMU graduates in the AIM specialty. This concept could be considered successful, and as the concept itself can also be well integrated into the teaching of other medical specialties, the potential of motivation-based teaching should be used more broadly within medical education.

• Publikační typ
• časopisecké články MeSH