The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI’s potential and limitations for enhancing clinicians’ skills in research, applied statistics and care delivery.

Introduction: The cardiovascular (CV) system produces low frequency, ‘infrasonic’, auditory vibrations during the cardiac cycle. Herein, we report the first-in-person validation of a novel earbud sensor to capture CV time intervals and the feasibility of non-invasive infrasonic hemodynography (IH) using the MindMics ® wireless earbuds for long term in-ear CV monitoring. Methods: Infrasonic waveforms were captured during cardiac catheterization (CC) among 5 study subjects wearing the IH ear-buds (Figure A) who underwent CC for the evaluation of coronary artery disease. Simultaneous IH and CC waveforms were acquired and time synchronized at 1000Hz sampling rate as time-series datasets. Each subject underwent echocardiography to identify aortic valve opening/closure (AVO/AVC) and left ventricular (LV) outflow tract flow measurements with hemodynamic waveforms during CC measuring LV ejection time (LVET). Validation of the IH waveform (in-ear acoustic pressure measured in Pascals) was compared to echocardiography (AVO/AVC) and hemodynamic waveforms (LVET) with concordance and Bland-Altman analysis, and with overlaid data visualizations to CV time intervals. Results: 5 study subjects comprised 257 CV cycles with a total data set of >450,000 time-series data points. IH signals collected simultaneously with the pulsed wave Doppler demonstrated alignment with AVO/AVC (Figure B) and were synchronized to CC waveforms in the aorta (Figure C). A high correlation between LVET measured from IH and CC was observed (R=0.87, p<0.0001, Figure D), with a mean absolute error of 14.7ms and a bias of 7.2ms (Figure E) (mean±SEM of 342.3±2.1ms for CC and 349.5±2.1ms for IH). Conclusions: In a first-in-person study, we report high accuracy between IH, echocardiography, and CC hemodynamic waveforms to capture CV time intervals including CV performance measures. Further studies are underway to validate IH and the earbud sensor towards non-invasive hemodynamic monitoring.

Aim: The COVID-19 pandemic forced medical practices to augment healthcare delivery to remote and virtual services. We describe the results of a nationwide survey of cardiovascular professionals regarding telehealth perspectives. Materials & methods: A 31-question survey was sent early in the pandemic to assess the impact of COVID-19 on telehealth adoption & reimbursement. Results: A total of 342 clinicians across 42 states participated. 77% were using telehealth, with the majority initiating usage 2 months after the COVID-19 shutdown. A variety of video-based systems were used. Telehealth integration requirements differed, with electronic medical record integration being mandated in more urban than rural practices (70 vs 59%; p < 0.005). Many implementation barriers surfaced, with over 75% of respondents emphasizing reimbursement uncertainty and concerns for telehealth generalizability given the complexity of cardiovascular diseases. Conclusion: Substantial variation exists in telehealth practices. Further studies and legislation are needed to improve access, reimbursement and the quality of telehealth-based cardiovascular care.

Human bodily mechanisms and functions produce low-frequency vibrations. Our ability to perceive these vibrations is limited by our range of hearing. However, in-ear infrasonic hemodynography (IH) can measure low-frequency vibrations (<20 Hz) created by vital organs as an acoustic waveform. This is captured using a technology that can be embedded into wearable devices such as in-ear headphones. IH can acquire sound signals that travel within arteries, fluids, bones, and muscles in proximity to the ear canal, allowing for measurements of an individual’s unique audiome. We describe the heart rate and heart rhythm results obtained in time-series analysis of the in-ear IH data taken simultaneously with ECG recordings in two dedicated clinical studies. We demonstrate a high correlation (r = 0.99) between IH and ECG acquired interbeat interval and heart rate measurements and show that IH can continuously monitor physiological changes in heart rate induced by various breathing exercises. We also show that IH can differentiate between atrial fibrillation and sinus rhythm with performance similar to ECG. The results represent a demonstration of IH capabilities to deliver accurate heart rate and heart rhythm measurements comparable to ECG, in a wearable form factor. The development of IH shows promise for monitoring acoustic imprints of the human body that will enable new real-time applications in cardiovascular health that are continuous and noninvasive.