Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases' diagnosis, and it can play a great role in elderly care and patient's health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.

/pdf/machine-learning-for-healthcare-wearable-devices-the-big-1hff6q6z.pdf

Machine Learning for Healthcare Wearable Devices: The Big Picture

The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.

https://www.mdpi.com/2075-4418/12/10/2549/pdf?version=1666682164

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate-to-severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate-to-severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm's ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.

/pdf/deep-learning-algorithm-for-automated-cardiac-murmur-7htf6f6zou.pdf

Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform.

Digital Technology Application for Improved Responses to Healthcare Challenges: Lessons Learned from COVID-19

Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all. 

Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice

Background There is variability among clinicians in their ability to detect murmurs during cardiac auscultation and identify the underlying pathology. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. Objective The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods Using over 34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 373 patients in a clinical study and collected recordings at the four primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by three expert cardiologists. Results Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1, sensitivity increases to 90.0%. The algorithm detects moderate-to-severe or greater aortic stenosis with sensitivity of 97.5% and specificity of 77.7% and detects moderate-to-severe or greater mitral regurgitation with sensitivity of 64.0% and specificity of 90.5%. Conclusion The deep learning algorithm’s ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists. The research findings attest to the reliability and utility of such algorithms as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease.

https://medrxiv.org/content/10.1101/2020.04.01.20050518v1.full.pdf

A Deep Learning Algorithm for Automated Cardiac Murmur Detection Via a Digital Stethoscope Platform

Background: Mitral regurgitation (MR) is a common disease which can be detected as a murmur on auscultation, but studies show that the majority of new primary care physicians do not detect MR murmu...

Abstract 13831: Handheld Wireless Digital Phonocardiography for Machine Learning-Based Detection of Mitral Regurgitation

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Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform.

A Deep Learning Algorithm for Automated Cardiac Murmur Detection Via a Digital Stethoscope Platform

Abstract 13831: Handheld Wireless Digital Phonocardiography for Machine Learning-Based Detection of Mitral Regurgitation