Integrating VAI-Assisted Quantified CXRs and Multimodal Data to Assess the Risk of Mortality.

To address the unmet need for a widely available examination for mortality prediction, this study developed a foundation visual artificial intelligence (VAI) to enhance mortality risk stratification using chest X-rays (CXRs). The VAI employed deep learning to extract CXR features and a Cox proportional hazard model to generate a hazard score ("CXR-risk"). We retrospectively collected CXRs from patients visited outpatient department and physical examination center.

Pitavastatin attenuates hypercholesterolemia-induced decline in serotonin transporter availability.

Introduction: Hypercholesterolemia is associated with increased inflammation and impaired serotonin neurotransmission, potentially contributing to depressive symptoms. However, the role of statins, particularly pitavastatin, in modulating serotonin transporter (SERT) function within this context remains underexplored. This study aimed to investigate whether pitavastatin counteracts the neurobiological effects of hypercholesterolemia.

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. Artificial Intelligence (AI) has demonstrated the capability to identify ePAP and its association with hospitalization due to heart failure when analyzing chest X-rays (CXR).

Enhancing the Central Venous Catheterization Competency of Medical Students through a Specialized Team and an Interactive Response System: A pre-post study.

Background: Central venous catheterization (CVC) is a critical clinical procedure. To avoid complications, possessing good knowledge regarding the CVC care bundle and skills for the proper insertion and maintenance of CVC are important.

Objectives: To evaluate the effectiveness of an educational intervention and the use of an interactive response system in enhancing the CVC bundle care and insertion skills of medical students undergoing critical care medicine training.

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events.

Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial.

Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening for high-risk populations. Purpose To evaluate the effectiveness of dual-energy x-ray absorptiometry (DXA) screening in high-risk patients with osteoporosis identified by an artificial intelligence (AI) model using chest radiographs. Materials and Methods This randomized controlled trial conducted at an academic medical center included participants 40 years of age or older who had undergone chest radiography between January and December 2022 without a history of DXA examination.

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

Background: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation.

AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.

The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality.

Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction.

Background: Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction.

Ca3.1 T-type calcium channel blocker NNC 55-0396 reduces atherosclerosis by increasing cholesterol efflux.

Calcium channel blockers (CCBs) are commonly used as antihypertensive agents. While certain L-type CCBs exhibit antiatherogenic effects, the impact of Ca3.1 T-type CCBs on antiatherogenesis and lipid metabolism remains unexplored.

Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk.

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis.

Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction With the Use of Electrocardiographic Artificial Intelligence: A Cost-Effectiveness Approach.

Background: The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm for asymptomatic LVD detection and evaluate its cost-effectiveness for opportunistic screening.

Methods: In this prospective observational study, patients undergoing ECG at outpatient clinics or health check-ups were enrolled in 2 hospitals in Taiwan.

A Deep-Learning Algorithm-Enhanced Electrocardiogram Interpretation for Detecting Pulmonary Embolism.

Background: The early diagnosis of pulmonary embolism (PE) remains a challenge. Electrocardiograms (ECGs) and D-dimer levels are used to screen potential cases.

Objective: To develop a deep learning model (DLM) to detect PE using ECGs and investigate the clinical value of false detections in patients without PE.

Artificial Intelligence-Enabled Electrocardiography Detects B-Type Natriuretic Peptide and N-Terminal Pro-Brain Natriuretic Peptide.

The B-type natriuretic peptide (BNP) and N-terminal pro-brain natriuretic peptide (pBNP) are predictors of cardiovascular morbidity and mortality. Since the artificial intelligence (AI)-enabled electrocardiogram (ECG) system is widely used in the management of many cardiovascular diseases (CVDs), patients requiring intensive monitoring may benefit from an AI-ECG with BNP/pBNP predictions. This study aimed to develop an AI-ECG to predict BNP/pBNP and compare their values for future mortality.

An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration.

Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations.

Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases.

Background: The electrocardiogram (ECG) may be the most popular test in the management of cardiovascular disease (CVD). Although wide applications of artificial intelligence (AI)-enabled ECG have been developed, an integrating indicator for CVD risk stratification was not investigated. Since mortality may be the most important global outcome, this study aimed to develop a survival deep learning model (DLM) to establish a critical ECG value and explore the associations with various CVD events.

Application of Sonographic Assessments of the Rate of Proximal Progression to Monitor Bite-Related Local Envenomation: A Prospective Observational Study.

Patients bitten by typically experience significant pain, substantial swelling, and potentially blister formation. The appropriate dosage and efficacy of FHAV for alleviating local tissue injury remain uncertain. Between 2017 and 2022, 29 snakebite patients were identified as being bitten by .

Peroxisome proliferator-activated receptor δ improves the features of atherosclerotic plaque vulnerability by regulating smooth muscle cell phenotypic switching.

Background And Purpose: Vascular smooth muscle cells (SMCs) undergo phenotypic switching during sustained inflammation, contributing to an unfavourable atherosclerotic plaque phenotype. PPARδ plays an important role in regulating SMC functions; however, its role in atherosclerotic plaque vulnerability remains unclear. Here, we explored the pathological roles of PPARδ in atherosclerotic plaque vulnerability in severe atherosclerosis and elucidated the underlying mechanisms.

Development and validation of a dynamic deep learning algorithm using electrocardiogram to predict dyskalaemias in patients with multiple visits.

Aims: Deep learning models (DLMs) have shown superiority in electrocardiogram (ECG) analysis and have been applied to diagnose dyskalaemias. However, no study has explored the performance of DLM-enabled ECG in continuous follow-up scenarios. Therefore, we proposed a dynamic revision of DLM-enabled ECG to use personal pre-annotated ECGs to enhance the accuracy in patients with multiple visits.

Extensive deep learning model to enhance electrocardiogram application via latent cardiovascular feature extraction from identity identification.

Background And Objective: Deep learning models (DLMs) have been successfully applied in biomedicine primarily using supervised learning with large, annotated databases. However, scarce training resources limit the potential of DLMs for electrocardiogram (ECG) analysis.

Methods: We have developed a novel pre-training strategy for unsupervised identity identification with an area under the receiver operating characteristic curve (AUC) >0.

Takotsubo Syndrome Occurring after mRNA COVID-19 Vaccination in a Patient with Graves' Disease.

Cardiovascular events such as myocarditis following mRNA COVID-19 vaccination are increasing. We present a 67-year-old postmenopausal woman with Takotsubo Syndrome and Graves' disease after mRNA COVID-19 vaccination. She developed chest pain and shortness of breath one week after vaccination.

Artificial intelligence-enabled electrocardiogram screens low left ventricular ejection fraction with a degree of confidence.

Background: Artificial intelligence-enabled electrocardiogram has become a substitute tool for echocardiography in left ventricular ejection fraction estimation. However, the direct use of artificial intelligence-enabled electrocardiogram may be not trustable due to the uncertainty of the prediction.

Objective: The study aimed to establish an artificial intelligence-enabled electrocardiogram with a degree of confidence to identify left ventricular dysfunction.

Effect of air quality improvement by urban parks on mitigating PM and its associated heavy metals: A mobile-monitoring field study.

Field mobile monitoring of PM, equipped with a highly accurate device, was performed for two types of urban parks in Taiwan. Measurements were taken in the morning and evening rush hours, on certain weekdays and weekends, every month over a year. We designed six calculation schemes of the rate of PM mitigation by urban parks to comprehensively compare the average and maximum mitigation effects in relation to the vegetation barriers.

The safety and efficacy of the Mo.Ma system device for carotid artery stenting: A single-center experience from Taiwan.

Background: Proximal protection devices, such as the Mo.Ma system provides better neurological outcomes than the distal filter system in the carotid artery stenting (CAS) procedure. This study first evaluated the safety and efficacy of the Mo.

Artificial intelligence-enabled electrocardiography identifies severe dyscalcemias and has prognostic value.

Context: Abnormal serum calcium concentrations affect the heart and may alter the electrocardiogram (ECG), but the detection of hypocalcemia and hypercalcemia (collectively dyscalcemia) relies on blood laboratory tests requiring turnaround time.

Objective: The study aimed to develop a bloodless artificial intelligence (AI)-enabled (ECG) method to rapidly detect dyscalcemia and analyze its possible utility for outcome prediction.

Methods: This study collected 86,731 development, 15,611 tuning, 11,105 internal validation, and 8401 external validation ECGs from electronic medical records with at least 1 ECG associated with an albumin-adjusted calcium (aCa) value within 4 h.