Barrier function-based prescribed performance trajectory tracking control of wheelchair upper-limb exoskeleton robot under actuator fault and external disturbance: Experimental verification.

This paper presents an innovative control strategy for the trajectory tracking of wheelchair upper-limb exoskeleton robots, integrating sliding mode control with a barrier function-based prescribed performance approach to handle actuator faults and external disturbances. The dynamic model of the exoskeleton robot is first extended to account for these uncertainties. The control design is then divided into two phases.

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.

Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias.

Deep convolutional neural network for rib fracture recognition on chest radiographs.

Introduction: Rib fractures are a prevalent injury among trauma patients, and accurate and timely diagnosis is crucial to mitigate associated risks. Unfortunately, missed rib fractures are common, leading to heightened morbidity and mortality rates. While more sensitive imaging modalities exist, their practicality is limited due to cost and radiation exposure.

Semantic Visualization in Functional Recovery Prediction of Intravenous Thrombolysis following Acute Ischemic Stroke in Patients by Using Biostatistics: An Exploratory Study.

(1) Background: Intravenous thrombolysis following acute ischemic stroke (AIS) can reduce disability and increase the survival rate. We designed a functional recovery analysis by using semantic visualization to predict the recovery probability in AIS patients receiving intravenous thrombolysis; (2) Methods: We enrolled 131 AIS patients undergoing intravenous thrombolysis from 2011 to 2015 at the Medical Center in northern Taiwan. An additional 54 AIS patients were enrolled from another community hospital.

Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test.

Background: Attention deficit hyperactivity disorder (ADHD) is a well-studied topic in child and adolescent psychiatry. ADHD diagnosis relies on information from an assessment scale used by teachers and parents and psychological assessment by physicians; however, the assessment results can be inconsistent.

Purpose: To construct models that automatically distinguish between children with predominantly inattentive-type ADHD (ADHD-I), with combined-type ADHD (ADHD-C), and without ADHD.

Automatic detection and vascular territory classification of hyperacute staged ischemic stroke on diffusion weighted image using convolutional neural networks.

Automated ischemic stroke detection and classification according to its vascular territory is an essential step in stroke image evaluation, especially at hyperacute stage where mechanical thrombectomy may improve patients' outcome. This study aimed to evaluate the performance of various convolutional neural network (CNN) models on hyperacute staged diffusion-weighted images (DWI) for detection of ischemic stroke and classification into anterior circulation infarct (ACI), posterior circulation infarct (PCI) and normal image slices. In this retrospective study, 253 cases of hyperacute staged DWI were identified, downloaded and reviewed.

Effect of three clinical therapies on cytokines modulation in the hip articular cartilage and bone improvement in rat early osteonecrosis of the femoral head.

Background: Extracorporeal shockwave therapy (ESWT) and adipose-derived mesenchymal stem cells (ADSCs) have been used clinically for the treatment of osteonecrosis of the femoral head (ONFH). The study elucidated that ESWT, ADSCs, and combination therapy modulated pro-inflammatory cytokines in the articular cartilage and subchondral bone of early rat ONFH.

Methods: ESWT and ADSCs were prepared and isolated for treatment.

Using Artificial Intelligence to Establish Chest X-Ray Image Recognition Model to Assist Crucial Diagnosis in Elder Patients With Dyspnea.

Pneumonia and pulmonary edema are the most common causes of acute respiratory failure in emergency and intensive care. Airway maintenance and heart function preservation are two foundations for resuscitation. Laboratory examinations have been utilized for clinicians to early differentiate pneumonia and pulmonary edema; however, none can provide results as prompt as radiology examinations, such as portable chest X-ray (CXR), which can quickly deliver results without mobilizing patients.

SEMRES - A Triple Security Protected Blockchain Based Medical Record Exchange Structure.

Background And Objective: COVID-19, a serious infectious disease outbreak started in the end of 2019, has caused a strong impact on the overall medical system, which reflects the gap in the volume and capacity of medical services and highlights the importance of clinical data ex-change and application. The most important concerns of medical records in the medical field include data privacy, data correctness, and data security. By realizing these three goals, medical records can be made available to different hospital information systems to achieve the most complete medical care services.

Exploring the COVID-19 Pandemic as a Catalyst for Behavior Change Among Patient Health Record App Users in Taiwan: Development and Usability Study.

Background: During the COVID-19 pandemic, personal health records (PHRs) have enabled patients to monitor and manage their medical data without visiting hospitals and, consequently, minimize their infection risk. Taiwan's National Health Insurance Administration (NHIA) launched the My Health Bank (MHB) service, a national PHR system through which insured individuals to access their cross-hospital medical data. Furthermore, in 2019, the NHIA released the MHB software development kit (SDK), which enables development of mobile apps with which insured individuals can retrieve their MHB data.

Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation.

Purpose: Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, we developed a machine learning model with a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation.

Materials And Methods: The proposed artificial neural network model was implemented using the MATLAB ANN toolbox, based on stochastic gradient descent.

Plasma extracellular vesicles tau and β-amyloid as biomarkers of cognitive dysfunction of Parkinson's disease.

The contribution of circulatory tau and β-amyloid in Parkinson's disease (PD), especially the cognitive function, remains inconclusive. Extracellular vesicles (EVs) cargo these proteins throughout the bloodstream after they are directly secreted from many cells, including neurons. The present study aims to investigate the role of the plasma EV-borne tau and β-amyloid as biomarkers for cognitive dysfunction in PD by investigating subjects with mild to moderate stage of PD (n = 116) and non-PD controls (n = 46).

Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks.

Background: To identify the outcome-associated predictors and develop predictive models for patients receiving targeted temperature management (TTM) by artificial neural network (ANN).

Methods: The derived cohort consisted of 580 patients with cardiac arrest and ROSC treated with TTM between January 2014 and August 2019. We evaluated the predictive value of parameters associated with survival and favorable neurologic outcome.

Extracorporeal Shockwave Therapy Modulates the Expressions of Proinflammatory Cytokines IL33 and IL17A, and Their Receptors ST2 and IL17RA, within the Articular Cartilage in Early Avascular Necrosis of the Femoral Head in a Rat Model.

Avascular necrosis (AVN) of the femoral head (AVNFH) is a disease caused by injury to the blood supply of the femoral head, resulting in a collapse with osteonecrosis and damage to the articular cartilage. Extracorporeal shockwave therapy (ESWT) has been demonstrated to improve AVNFH owing to its anti-inflammation activity, angiogenesis effect, and tissue regeneration in clinical treatment. However, there are still so many pieces of the jigsaw that need to be fit into place in order to ascertain the mechanism of ESWT for the treatment of AVNFH.

Automatic classification of medical image modality and anatomical location using convolutional neural network.

Modern radiologic images comply with DICOM (digital imaging and communications in medicine) standard, which, upon conversion to other image format, would lose its image detail and information such as patient demographics or type of image modality that DICOM format carries. As there is a growing interest in using large amount of image data for research purpose and acquisition of large amount of medical image is now a standard practice in the clinical setting, efficient handling and storage of large amount of image data is important in both the clinical and research setting. In this study, four classes of images were created, namely, CT (computed tomography) of abdomen, CT of brain, MRI (magnetic resonance imaging) of brain and MRI of spine.

Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks.

Background: Accurate estimation of neurological outcomes after in-hospital cardiac arrest (IHCA) provides crucial information for clinical management. This study used artificial neural networks (ANNs) to determine the prognostic factors and develop prediction models for IHCA based on immediate preresuscitation parameters.

Methods: The derived cohort comprised 796 patients with IHCA between 2006 and 2014.

Application of machine learning-based models to boost the predictive power of the SPAN index.

Background: This study re-explored the predictive validity of Stroke Prognostication using Age and National Institutes of Health Stroke Scale (SPAN) index in patients who received different treatments for acute ischemic stroke (AIS) and developed machine learning-boosted outcome prediction models.

Methods: We evaluated the prognostic relevance of SPAN index in patients with AIS who received intravenous tissue-type plasminogen activator (IV-tPA), intra-arterial thrombolysis (IAT) or non-thrombolytic treatments (non-tPA), and applied machine learning algorithms to develop SPAN-based outcome prediction models in a cohort of 2145 hospitalized AIS patients. The performance of the models was assessed and compared using the area under the receiver operating characteristic curves (AUCs).

Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death.

Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study explored the use of artificial neural network (ANN)-based models to predict sICH and 3-month mortality for patients with AIS receiving tPA. We developed ANN models based on evaluation of the predictive value of pre-treatment parameters associated with sICH and mortality in a cohort of 331 patients between 2009 and 2018.

Increase Risk of Dementia in Patients With Sudden Hearing Loss: A Population-Based Cohort Study With 7-Year Follow-Up in Taiwan.

Objective: To investigate the prevalence and risk of subsequent dementia in subjects with sudden hearing loss during a 7-year follow-up period through comparisons with cohorts matched by sex, age group, and year of index date.

Study Design: A retrospective matched-cohort study.

Setting: The Longitudinal Health Insurance Database 2000 (LHID2000) in Taiwan.

Predicting major neurologic improvement and long-term outcome after thrombolysis using artificial neural networks.

Objective: To develop artificial neural network (ANN)-based functional outcome prediction models for patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis based on immediate pretreatment parameters.

Methods: The derived cohort consisted of 196 patients with AIS treated with intravenous thrombolysis between 2009 and 2017 at Shuang Ho Hospital in Taiwan. We evaluated the predictive value of parameters associated with major neurologic improvement (MNI) at 24 h after thrombolysis as well as the 3-month outcome.

Palmitic Acid Methyl Ester Induces G/M Arrest in Human Bone Marrow-Derived Mesenchymal Stem Cells via the p53/p21 Pathway.

Bone marrow-derived mesenchymal cells (BM-MSCs) are able to differentiate into adipocytes, which can secrete adipokines to affect BM-MSC proliferation and differentiation. Recent evidences indicated that adipocytes can secrete fatty acid metabolites, such as palmitic acid methyl ester (PAME), which is able to cause vasorelaxation and exerts anti-inflammatory effects. However, effects of PAME on BM-MSC proliferation remain unclear.

Association Between Psoriasis and Dementia: A Population-Based Case-Control Study.

Background: Dementia is a syndrome that involves the deterioration of several higher mental functions in advanced age, and psoriasis is an autoimmune disease characterized by skin plaque. Epidemiological studies have indicated an association between dementia and psoriasis; however, to date, no studies in Asia have reported this association.

Objective: This study used a population-based medical dataset to explore the association between previously diagnosed psoriasis and dementia in Taiwan.