Application of Mobile Health, Telemedicine and Artificial Intelligence to Echocardiography

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine

Application of mobile health, telemedicine and artificial intelligence to echocardiography

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine

Web Portals for Patients With Chronic Diseases:Scoping Review of the Functional Features and Theoretical Frameworks of Telerehabilitation Platforms

BACKGROUND: The COVID-19 pandemic has required an increased need for rehabilitation activities applicable to patients with chronic diseases. Telerehabilitation has several advantages, including reducing clinic visits by patients vulnerable to infectious diseases. Digital platforms are often used to assist rehabilitation services for patients in remote settings. Although web portals for medical use have existed for years, the technology in telerehabilitation remains a novel method. OBJECTIVE: This scoping review investigated the functional features and theoretical approaches of web portals developed for telerehabilitation in patients with chronic diseases. METHODS: PubMed and Web of Science were reviewed to identify articles associated with telerehabilitation. Of the 477 nonduplicate articles reviewed, 35 involving 14 portals were retrieved for the scoping review. The functional features, targeted diseases, and theoretical approaches of these portals were studied. RESULTS: The 14 portals targeted patients with chronic obstructive pulmonary disease, cardiovascular, osteoarthritis, multiple sclerosis, cystic fibrosis diseases, and stroke and breast cancer survivors. Monitoring/data tracking and communication functions were the most common, followed by exercise instructions and diary/self-report features. Several theoretical approaches, behavior change techniques, and motivational techniques were found to be utilized. CONCLUSIONS: The web portals could unify and display multiple types of data and effectively provide various types of information. Asynchronous correspondence was more favorable than synchronous, real-time interactions. Data acquisition often required assistance from other digital tools. Various functions with patient-centered principles, behavior change strategies, and motivational techniques were observed for better support shifting to a healthier lifestyle. These findings suggested that web portals for telerehabilitation not only provided entrance into rehabilitation programs but also reinforced participant-centered treatment, adherence to rehabilitation, and lifestyle changes over time

Effects of Telerehabilitation Interventions on Heart Failure Management (2015-2020):Scoping Review

BackgroundHeart failure is one of the world’s most frequently diagnosed cardiovascular diseases. An important element of heart failure management is cardiac rehabilitation, the goal of which is to improve patients’ recovery, functional capacity, psychosocial well-being, and health-related quality of life. Patients in cardiac rehabilitation may lack sufficient motivation or may feel that the rehabilitation process does not meet their individual needs. One solution to these challenges is the use of telerehabilitation. Although telerehabilitation has been available for several years, it has only recently begun to be utilized in heart failure studies. Especially within the past 5 years, we now have several studies focusing on the effectiveness of telerehabilitation for heart failure management, all with varying results. Based on a review of these studies, this paper offers an assessment of the effectiveness of telerehabilitation as applied to heart failure management. ObjectiveThe aim of this scoping review was to assess the effects of telerehabilitation in the management of heart failure by systematically reviewing the available scientific literature within the period from January 1, 2015, to December 31, 2020. MethodsThe literature search was carried out using PubMed and EMBASE. After duplicates were removed, 77 articles were screened and 12 articles were subsequently reviewed. The review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for scoping reviews) guidelines. As measures of the effectiveness of telerehabilitation, the following outcomes were used: patients’ quality of life, physical capacity, depression or anxiety, and adherence to the intervention. ResultsA total of 12 articles were included in this review. In reviewing the effects of telerehabilitation for patients with heart failure, it was found that 4 out of 6 randomized controlled trials (RCTs), a single prospective study, and 4 out of 5 reviews reported increased quality of life for patients. For physical capacity, 4 RCTs and 3 systematic reviews revealed increased physical capacity. Depression or depressive symptoms were reported as being reduced in 1 of the 6 RCTs and in 2 of the 5 reviews. Anxiety or anxiety-related symptoms were reported as reduced in only 1 review. High adherence to the telerehabilitation program was reported in 4 RCTs and 4 reviews. It should be mentioned that some of the reviewed articles described the same studies although they employed different outcome measures. ConclusionsIt was found that there is a tendency toward improvement in patients’ quality of life and physical capacity when telerehabilitation was used in heart failure management. The outcome measures of depression, anxiety, and adherence to the intervention were found to be positive. Additional research is needed to determine more precise and robust effects of telerehabilitation

Multimodality imaging in takotsubo syndrome : a joint consensus document of the European Association of Cardiovascular Imaging (EACVI) and the Japanese Society of Echocardiography (JSE)

This article is co-published in the journals the European Heart Journal—Cardiovascular Imaging https://doi.org/10.1093/ehjci/jeaa149 and Journal of Echocardiography https://doi.org/10.1007/s12574-020-00480-y.Peer reviewedPublisher PD

Physiological and prognostic differences between types of exercise stress echocardiography for functional mitral regurgitation

Objective Secondary mitral regurgitation (MR) demonstrates dynamic change during exercise. This prospective observational study aimed to compare exercise stress echocardiography (ESE) where handgrip exercise (handgrip-ESE) or semisupine ergometer exercise was performed (ergometer-ESE) for patients with secondary MR.Methods Handgrip-ESE and symptom-limited ergometer-ESE were performed for 53 patients (median age (IQR): 68 (58–78) years; 70% male) on the same day. Baseline global longitudinal strain (GLS) was 9.2% (6.0%–14.0%) and MR volume was 20 (14–26) mL. All-cause death and cardiac hospitalisation were tracked for median 439 (101–507) days.Results Handgrip-ESE induced slightly but significantly greater degrees of MR increase (median one grade increase; p<0.001) than ergometer-ESE, although the changes in other parameters, including GLS (+1.1% vs −0.6%, p<0.001), were significantly smaller. Correlations between the two examinations with respect to the changes in the echocardiographic parameters were weak. Kaplan-Meier analyses revealed poor improvement in GLS during ergometer-ESE, but not the change in MR, was associated with adverse events (p=0.0065). No echocardiographic change observed during handgrip-ESE was prognostic. After adjusting for a clinical risk score, GLS changes during ergometer-ESE remained significant in predicting the adverse events (HR 0.39, p=0.03) A subgroup analysis in patients with moderate or greater MR at baseline (n=27) showed the same results as in the entire cohort.Conclusions The physiological and prognostic implications of handgrip-ESE and ergometer-ESE findings significantly differ in patients with left ventricular dysfunction and secondary MR. The type of exercise to be performed in ESE should be carefully selected

Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study

AimsCoronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms.Methods and resultsIn this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years.ConclusionML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease