How to assess and treat right ventricular electromechanical dyssynchrony in post-repair tetralogy of Fallot: insights from imaging, invasive studies, and computational modelling.

BACKGROUND AND AIMS: Right bundle branch block (RBBB) and resulting right ventricular (RV) electromechanical discoordination are thought to play a role in the disease process of subpulmonary RV dysfunction that frequently occur post-repair tetralogy of Fallot (ToF). We sought to describe this disease entity, the role of pulmonary re-valvulation, and the potential added value of RV cardiac resynchronization therapy (RV-CRT). METHODS: Two patients with repaired ToF, complete RBBB, pulmonary regurgitation, and significantly decreased RV function underwent echocardiography, cardiac magnetic resonance, and an invasive study to evaluate the potential for RV-CRT as part of the management strategy. The data were used to personalize the CircAdapt model of the human heart and circulation. Resulting Digital Twins were analysed to quantify the relative effects of RV pressure and volume overload and to predict the effect of RV-CRT. RESULTS: Echocardiography showed components of a classic RV dyssynchrony pattern which could be reversed by RV-CRT during invasive study and resulted in acute improvement in RV systolic function. The Digital Twins confirmed a contribution of electromechanical RV dyssynchrony to RV dysfunction and suggested improvement of RV contraction efficiency after RV-CRT. The one patient who underwent successful permanent RV-CRT as part of the pulmonary re-valvulation procedure carried improvements that were in line with the predictions based on his Digital Twin. CONCLUSION: An integrative diagnostic approach to RV dysfunction, including the construction of Digital Twins may help to identify candidates for RV-CRT as part of the lifetime management of ToF and similar congenital heart lesions.

Artificial intelligence in medicine and healthcare: Opportunity and/or threat.

The aim of the article to present the development of artificial intelligence (AI) methods and their applications in medicine and health care. Current technological development contributes to generation of large volumes of data that cannot be evaluated only manually. We describe the process of patient care and its individual parts that can be supported by technology and data analysis methods. There are many successful applications that help in the decision support process, in processing complex multidimensional heterogeneous and/or long-term data. On the other side, failures appear in AI methods applications. In recent years, deep learning became very popular and to a certain extend it delivered promising results. However, it has certain flaws that might lead to misclassification. The correct methodological steps in design and implementation of selected methods to data processing are briefly presented.

The way to data: opinions and recommendations for the provision of health data for secondary use.

Healthcare data held by state-run organisations is a valuable intangible asset for society. Its use should be a priority for its administrators and the state. A completely paternalistic approach by administrators and the state is undesirable, however much it aims to protect the privacy rights of persons registered in databases. In line with European policies and the global trend, these measures should not outweigh the social benefit that arises from the analysis of these data if the technical possibilities exist to sufficiently protect the privacy rights of individuals. Czech society is having an intense discussion on the topic, but according to the authors, it is insufficiently based on facts and lacks clearly articulated opinions of the expert public. The aim of this article is to fill these gaps. Data anonymization techniques provide a solution to protect individuals' privacy rights while preserving the scientific value of the data. The risk of identifying individuals in anonymised data sets is scalable and can be minimised depending on the type and content of the data and its use by the specific applicant. Finding the optimal form and scope of deidentified data requires competence and knowledge on the part of both the applicant and the administrator. It is in the interest of the applicant, the administrator, as well as the protected persons in the databases that both parties show willingness and have the ability and expertise to communicate during the application and its processing.

Boosting phase-contrast MRI performance in idiopathic normal pressure hydrocephalus diagnostics by means of machine learning approach.

OBJECTIVE: Phase-contrast MRI allows detailed measurements of various parameters of CSF motion. This examination is technically demanding and machine dependent. The literature on this topic is ambiguous. Machine learning (ML) approaches have already been successfully utilized in medical research, but none have yet been applied to enhance the results of CSF flowmetry. The aim of this study was to evaluate the possible contribution of ML algorithms in enhancing the utilization and results of MRI flowmetry in idiopathic normal pressure hydrocephalus (iNPH) diagnostics. METHODS: The study cohort consisted of 30 iNPH patients and 15 healthy controls examined on one MRI machine. All major phase-contrast parameters were inspected: peak positive, peak negative, and average velocities; peak amplitude; positive, negative, and average flow rates; and aqueductal area. The authors applied ML algorithms to 85 complex features calculated from a phase-contrast study. RESULTS: The most distinctive parameters with p < 0.005 were the peak negative velocity, peak amplitude, and negative flow. From the ML algorithms, the Adaptive Boosting classifier showed the highest specificity and best discrimination potential overall, with 80.4% ± 2.9% accuracy, 72.0% ± 5.6% sensitivity, 84.7% ± 3.8% specificity, and 0.812 ± 0.047 area under the receiver operating characteristic curve (AUC). The highest sensitivity was 85.7% ± 5.6%, reached by the Gaussian Naive Bayes model, and the best AUC was 0.854 ± 0.028 by the Extra Trees classifier. CONCLUSIONS: Feature extraction algorithms combined with ML approaches simplify the utilization of phase-contrast MRI. The highest-performing ML algorithm was Adaptive Boosting, which showed good calibration and discrimination on the testing data, with 80.4% accuracy, 72.0% sensitivity, 84.7% specificity, and 0.812 AUC. Phase-contrast MRI boosted by the ML approach can help to determine shunt-responsive iNPH patients.

Solutions to Gender Balance in STEM Fields Through Support, Training, Education and Mentoring: Report of the International Women in Medical Physics and Biomedical Engineering Task Group.

The aim of this article is to offer a view of the current status of women in medical physics and biomedical engineering, while focusing on solutions towards gender balance and providing examples of current activities carried out at national and international levels. The International Union of Physical and Engineering Scientists in Medicine is committed to advancing women in science and health and has several initiatives overseen by the Women in Medical Physics and Biomedical Engineering Task Group. Some of the main strategies proposed by the Task Group to attain gender balance are: (a) identify and promote female role models that achieve successful work-life balance, (b) establish programs to develop female leaders, (c) create opportunities for females to increase the international visibility within the scientific community, and (d) establish archives and databases of women in STEM.

An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry.

In biomedical signal processing, we often face the problem of artifacts that distort the original signals. This concerns also sleep recordings, such as EEG. Artifacts may severely affect or even make impossible visual inspection, as well as automatic processing. Many proposed methods concentrate on certain artifact types. Therefore, artifact-free data are often obtained after sequential application of different methods. Moreover, single-channel approaches must be applied to all channels alternately. The aim of this study is to develop a multichannel artifact detection method for multichannel sleep EEG capable of rejecting different artifact types at once. The inspiration for the study is gained from recent advances in the field of Riemannian geometry. The method we propose is tested on real datasets. The performance of the proposed method is measured by comparing detection results with the expert labeling as a reference and evaluated against a simpler method based on Riemannian geometry that has previously been proposed, as well as against the state-of-the-art method FASTER. The obtained results prove the effectiveness of the proposed method.

Biomechanical analysis of INFINITY rehabilitation method for treatment of low back pain.

[Purpose] Low back pain is a pervasive problem in modern societies. Physical rehabilitation in treatment of low back pain should reduce pain, muscle tension and restore spine stability and balance. The INFINITY® rehabilitation method that is based on a figure of eight movement pattern was proved to be effective in low back pain treatment. The aim of the paper is to estimate the effect of a figure of eight motion on the L5/S1 load and lumbar spine muscle activation in comparison to other motion patterns. [Subjects and Methods] Three-dimensional model of lumbar spine musculoskeletal system is used to simulate effect of various load motion pattern induced by displacement of the center of gravity of the upper body. Four motion patterns were examined: lateral and oblique pendulum-like motion, elliptical motion and figure of eight motion. [Results] The simple pendulum-like and elliptical-like patterns induce harmonic muscle activation and harmonic spinal load. The figure of eight motion pattern creates high-frequency spinal loading that activates remodeling of bones and tendons. The figure of eight pattern also requires muscle activity that differs from harmonic frequency and is more demanding on muscle control and could also improve muscle coordination. [Conclusion] The results of the study indicate that complex motion pattern during INFINITY® rehabilitation might enhance the spine stability by influencing its passive, active and neural components.

Specific rehabilitation exercise for the treatment of patients with chronic low back pain.

[Purpose] To evaluate the efficacy of our special rehabilitation method for patients with low back pain (LBP). [Subjects and Methods] All participants (n=33) received at least five individual 30-minute therapy sessions per week using the INFINITY method(®) and six group therapy sessions per week in a gymnasium and swimming pool, each lasting 30 minutes and including the INFINITY method(®). The treatment lasted between four to seven weeks. Plantar function using a graphic method (computer plantography), graphical quantification of postural control during static standing (posturography), and pain were measured and evaluated before and after rehabilitation therapy. The INFINITY method(®) is a special rehabilitation method for patients with musculoskeletal problems. The method focuses on stabilization and strengthening of the trunk, dorsal and abdominal muscles, including the deep stabilization system which is closely linked with diaphragmatic breathing. It teaches the central nervous system to control muscles more precisely. [Results] Plantar functions, postural control in the upright stance and pain of LBP patients were significantly improved by 4-7 weeks of rehabilitation treatment with the INFINITY method(®). There were significant differences in all measured dependent variables of the patients between before and after treatment. [Conclusion] Rehabilitation therapy with the INFINITY method(®) positively influences body stabilization and pain in patients with problems of the lumbar spine. This method presents a new improved approach (with enhanced effect) to rehabilitation therapy for LBP patients.

Analysis of obstetricians' decision making on CTG recordings.

Interpretation of cardiotocogram (CTG) is a difficult task since its evaluation is complicated by a great inter- and intra-individual variability. Previous studies have predominantly analyzed clinicians' agreement on CTG evaluation based on quantitative measures (e.g. kappa coefficient) that do not offer any insight into clinical decision making. In this paper we aim to examine the agreement on evaluation in detail and provide data-driven analysis of clinical evaluation. For this study, nine obstetricians provided clinical evaluation of 634 CTG recordings (each ca. 60min long). We studied the agreement on evaluation and its dependence on the increasing number of clinicians involved in the final decision. We showed that despite of large number of clinicians the agreement on CTG evaluations is difficult to reach. The main reason is inherent inter- and intra-observer variability of CTG evaluation. Latent class model provides better and more natural way to aggregate the CTG evaluation than the majority voting especially for larger number of clinicians. Significant improvement was reached in particular for the pathological evaluation - giving a new insight into the process of CTG evaluation. Further, the analysis of latent class model revealed that clinicians unconsciously use four classes when evaluating CTG recordings, despite the fact that the clinical evaluation was based on FIGO guidelines where three classes are defined.

Open access intrapartum CTG database.

BACKGROUND: Cardiotocography (CTG) is a monitoring of fetal heart rate and uterine contractions. Since 1960 it is routinely used by obstetricians to assess fetal well-being. Many attempts to introduce methods of automatic signal processing and evaluation have appeared during the last 20 years, however still no significant progress similar to that in the domain of adult heart rate variability, where open access databases are available (e.g. MIT-BIH), is visible. Based on a thorough review of the relevant publications, presented in this paper, the shortcomings of the current state are obvious. A lack of common ground for clinicians and technicians in the field hinders clinically usable progress. Our open access database of digital intrapartum cardiotocographic recordings aims to change that. DESCRIPTION: The intrapartum CTG database consists in total of 552 intrapartum recordings, which were acquired between April 2010 and August 2012 at the obstetrics ward of the University Hospital in Brno, Czech Republic. All recordings were stored in electronic form in the OB TraceVue®;system. The recordings were selected from 9164 intrapartum recordings with clinical as well as technical considerations in mind. All recordings are at most 90 minutes long and start a maximum of 90 minutes before delivery. The time relation of CTG to delivery is known as well as the length of the second stage of labor which does not exceed 30 minutes. The majority of recordings (all but 46 cesarean sections) is - on purpose - from vaginal deliveries. All recordings have available biochemical markers as well as some more general clinical features. Full description of the database and reasoning behind selection of the parameters is presented in the paper. CONCLUSION: A new open-access CTG database is introduced which should give the research community common ground for comparison of results on reasonably large database. We anticipate that after reading the paper, the reader will understand the context of the field from clinical and technical perspectives which will enable him/her to use the database and also understand its limitations.

Perspectives on the COVID-19 Vaccination Rollout in 17 Countries: Reflexive Thematic and Frequency Analysis Based on the Strengths, Weaknesses, Opportunities, and Threats (SWOT) Framework.

BACKGROUND: As the SARS-CoV-2 virus created a global pandemic and rapidly became an imminent threat to the health and lives of people worldwide, the need for a vaccine and its quick distribution among the population was evident. Due to the urgency, and on the back of international collaboration, vaccines were developed rapidly. However, vaccination rollouts showed different success rates in different countries and some also led to increased vaccine hesitancy. OBJECTIVE: The aim of this study was to identify the role of information sharing and context sensitivity in various vaccination programs throughout the initial COVID-19 vaccination rollout in different countries. Moreover, we aimed to identify factors in national vaccination programs related to COVID-19 vaccine hesitancy, safety, and effectiveness. Toward this end, multidisciplinary and multinational opinions from members of the Navigating Knowledge Landscape (NKL) network were analyzed. METHODS: From May to July 2021, 25 completed questionnaires from 27 NKL network members were collected. These contributors were from 17 different countries. The responses reflected the contributors' subjective viewpoints on the status and details of the COVID-19 vaccination rollout in their countries. Contributors were asked to identify strengths, weaknesses, opportunities, and threats (ie, SWOT) of the respective vaccination programs. The responses were analyzed using reflexive thematic analysis, followed by frequency analysis of identified themes according to the represented countries. RESULTS: The perspectives of NKL network members showed a link between organizational elements of the vaccination rollout and the accompanying societal response, both of which were related to strengths and weaknesses of the process. External sociocultural variables, improved public communication around vaccination-related issues, ethical controversies, and the spread of disinformation were the dominant themes related to opportunities and challenges. In the SWOT 2×2 matrix, Availability and Barriers emerged as internal categories, whereas Transparent communication and promotion and Societal divide emerged as key external categories. CONCLUSIONS: Inventory of themes and categories inspired by elements of the SWOT framework provides an informative multidisciplinary perspective for effective implementation of public health strategies in the battle against COVID-19 or any future pandemics of a similar nature.

Using EfficientNet-B7 (CNN), Variational Auto Encoder (VAE) and Siamese Twins' Networks to Evaluate Human Exercises as Super Objects in a TSSCI Images.

In this article, we introduce a new approach to human movement by defining the movement as a static super object represented by a single two-dimensional image. The described method is applicable in remote healthcare applications, such as physiotherapeutic exercises. It allows researchers to label and describe the entire exercise as a standalone object, isolated from the reference video. This approach allows us to perform various tasks, including detecting similar movements in a video, measuring and comparing movements, generating new similar movements, and defining choreography by controlling specific parameters in the human body skeleton. As a result of the presented approach, we can eliminate the need to label images manually, disregard the problem of finding the start and the end of an exercise, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one illustrates how to verify and score a fitness exercise. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supplying sufficient training data for deep learning applications (DL). A variational auto encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate the two use cases. These use cases demonstrate the versatility of our innovative concept in measuring, categorizing, inferring human behavior, and generating gestures for other researchers.

A Pilot Study to Assess Hydration in Geriatric Patients by Bioimpedance.

Hydration plays a very important role in old age. This is because hydration changes over the course of life and therefore geriatric patients need to have their hydration monitored. However, the general problem is that there are no completely reliable methods' that can measure this. In this paper we performed a pilot monitoring in geriatric patients and compared directly measured electrical data with results from biochemistry. The observed correlations on our pilot sample show very promising values for (r=0.68) creatinine correlation with phase angle and (r=0.71) creatinine correlation with NI (nutritional index). It also shows that electrical readings may in the future indicate much more accurately the true status of the patient. Our research is still ongoing, and we are planning further measurements on a larger sample.

Evaluation of eHealth assistance in-hospital care for improved quality of life in patients.

Health conditions contribute significantly to patients' quality of life. Healthcare infrastructure and healthcare services, including their accessibility, belong to objective factors influencing their perception of their health. The growing disparity between supply and demand for specialized inpatient facilities due to the aging population calls for new solutions, including eHealth technologies. Automatized activities could be taken over by eHealth technologies that do not require a constant presence of staff. We tested whether eHealth technical solutions reduce patients' health risks on a sample of 61 patients on the covid-19 unit in Tomas Bata hospital in Zlin. We have applied the randomized control trial to select patients for the treatment and the control groups. Moreover, we tested eHealth technologies and their help to staff in the hospital. Due to the severity of the covid-19 disease and its rapid course and the size of the sample in our research, we did not demonstrate a statistically significant impact of eHealth technologies on patient health. The evaluation results confirm that even the limited number of technologies deployed proves to be an effective help for staff in critical situations like the pandemic. The main issue is psychological support to staff in hospitals and relieving stressful work.

Evaluation of functional tests performance using a camera-based and machine learning approach.

The objective of this study is to evaluate the performance of functional tests using a camera-based system and machine learning techniques. Specifically, we investigate whether OpenPose and any standard camera can be used to assess the quality of the Single Leg Squat Test and Step Down Test functional tests. We recorded these exercises performed by forty-six healthy subjects, extract motion data, and classify them to expert assessments by three independent physiotherapists using 15 binary parameters. We calculated ranges of movement in Keypoint-pair orientations, joint angles, and relative distances of the monitored segments and used machine learning algorithms to predict the physiotherapists' assessments. Our results show that the AdaBoost classifier achieved a specificity of 0.8, a sensitivity of 0.68, and an accuracy of 0.7. Our findings suggest that a camera-based system combined with machine learning algorithms can be a simple and inexpensive tool to assess the performance quality of functional tests.

Frames of reference and their neural correlates within navigation in a 3D environment.

The goal of this study was an administration of the navigation task in a three-dimensional virtual environment to localize the electroencephalogram (EEG) features responsible for egocentric and allocentric reference frame processing in a horizontal and also in a vertical plane. We recorded the EEG signal of a traverse through a virtual tunnel to search for the best signal features that discriminate between specific strategies in particular plane. We identified intrahemispheric coherences in occipital-parietal and temporal-parietal areas as the most discriminative features. They have 10% lower error rate compared to single electrode features adopted in previous studies. The behavioral analysis revealed that 11% of participants switched from egocentric to allocentric strategy in a vertical plane, while 24% of participants consistently adopted egocentric strategy in both planes.

Health data collecting and sharing: case studies of Czech e-health applications.

The paper shows the importance of e-health applications for electronic healthcare development. It describes several e-health applications for health data collecting and sharing that are running in the Czech Republic. These are IZIP system, electronic health record MUDR and K4CARE project applications. The e3-health concept is considered as a tool for judging e-health applications in different healthcare settings.

Assessing Human Mobility by Constructing a Skeletal Database and Augmenting it Using a Generative Adversarial Network (GAN) Simulator.

This paper presents a neural network simulator based on anonymized patient motions that measures, categorizes, and infers human gestures based on a library of anonymized patient motions. There is a need for a sufficient training set for deep learning applications (DL). Our proposal is to extend a database that includes a limited number of videos of human physiotherapy activities with synthetic data. As a result of our posture generator, we are able to generate skeletal vectors that depict human movement. A human skeletal model is generated by using OpenPose (OP) from multiple-person videos and photographs. In every video frame, OP represents each human skeletal position as a vector in Euclidean space. The GAN is used to generate new samples and control the parameters of the motion. The joints in our skeletal model have been restructured to emphasize their linkages using depth-first search (DFS), a method for searching tree structures. Additionally, this work explores solutions to common problems associated with the acquisition of human gesture data, such as synchronizing activities and linking them to time and space. A new simulator is proposed that generates a sequence of virtual coordinated human movements based upon a script.

Decentralized EHRs in the Semantic Web for Better Health Data Management.

Electronic Health Record (EHR) systems currently in use are not designed for widely interoperable longitudinal health data. Therefore, EHR data cannot be properly shared, managed and analyzed. In this article, we propose two approaches to making EHR data more comprehensive and FAIR (Findable, Accessible, Interoperable, and Reusable) and thus more useful for diagnosis and clinical research. Firstly, the data modeling based on the LinkML framework makes the data interoperability more realistic in diverse environments with various experts involved. We show the first results of how diverse health data can be integrated based on an easy-to-understand data model and without loss of available clinical knowledge. Secondly, decentralizing EHRs contributes to the higher availability of comprehensive and consistent EHR data. We propose a technology stack for decentralized EHRs and the reasons behind this proposal. Moreover, the two proposed approaches empower patients because their EHR data can become more available, understandable, and usable for them, and they can share their data according to their needs and preferences. Finally, we explore how the users of the proposed solution could be involved in the process of its validation and adoption.

Information System for Integration of Genetic Information and Clinical Knowledge.

This paper deals with a developed information system called a Personal Genetic Card (PGC). The system aims to integrate the known clinical knowledge (interpretations and recommendations) linked to genetic information with the analysis results of a patient. Genetic information has an increasing influence on the clinical decision of physicians as well as other medical and health services. All these services need to connect the genetic profile with the phenotypes such as drug metabolization, drug toxicity, drug dosing, or intolerance of some substances. It still applies that the best way to represent data of medical records is a structured form of record. Many approaches can be used to define the structure (syntax) of the record and the content (semantics) of the record and to exchange data in forms of various standards and terminologies. Moreover, the genetic analysis field has its terminology databases for representing genetic information (e.g. HGNC, NCBI). The next step is to connect the genetic analysis results with c clinical knowledge (interpretation, recommendation). This step is crucial because the genetic analysis results have clinical benefits if we can assign them to some valid clinical knowledge. And the best final result is when we can make a better recommendation based on the genetic results and clinical knowledge. Genetic knowledge databases (e.g. PharmGKB, SNPedia, ClinVar) contain many interpretations and even recommendations for genetic analysis results based on different purposes. This situation is appropriate for developing the PGC system that takes inspiration from case-based reasoning in purpose to allow integration of the assumptions and knowledge about phenotypes and the real genetic analysis results in the structured form.