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LABORATORY INFORMATICS


Wearable tech fuels AI research


SOPHIA KTORI DISCUSSES THE CONVERGENCE OF CLINICAL AI AND WEARABLE TECHNOLOGY WITH PHYSIQ CHAIRMAN AND CEO GARY CONKRIGHT


At the end of February physIQ and the US Department of Veteran’s Affairs published data


from a clinical trial demonstrating the use of physIQ’s machine-learning algorithm to predict when an individual patient with previously diagnosed heart failure will likely require a readmission to hospital. In the study, heart failure patients were


given disposable, wearable sensors, which collected physiological data that was streamed to physIQ’s pinpointIQ continuous data collection platform. Results from the trial, which was reported in Circulation – Heart Failure, showed that the AI-based algorithm could predict from the sensor data whether a patient was likely to be re-hospitalised up to 10 days before they were either readmitted, or had to go to the emergency room. ‘These results exceeded our


expectations and demonstrate the power of our FDA cleared AI to give clinicians a fighting chance in keeping late-stage chronically ill patients out of the hospital and at home. Most clinicians will tell you that, with several days of lead time to intervene, their chances of changing the trajectory of any disease exacerbation are very high,’ reported Gary Conkright, physIQ founder, chairman and CEO. Publication of the paper comes just


weeks after physIQ was granted two new US patents for patient monitoring AI-based data analytics. One of the new patents covers use of deep learning to make estimates of cardiopulmonary functional capacity using data from sensors on wearable devices. The second new patent is part of an expanding suite of patents relating to physIQ’s personalised physiology modelling technology. PhysIQ is exploiting its scalable


18 Scientific Computing World Spring 2020


“However, we have developed a novel way of leveraging our existing 2+ million hours of high- fidelity wearable data to significantly reduce the inputs needed for a deep learning generated algorithm”


cloud-based platform and proprietary AI technology to enable personalised physiology analytics, for what it terms proactive care delivery models. The FDA 510(k)-cleared data analytics technology is designed to process multiple vital signs from wearable sensors, and create a personalised, dynamic baseline for each individual. Subtle deviations from this baseline


identified by the algorithm can indicate worsening disease, or a change in health, and alert the physician. PhysIQ is leveraging the platform for applications in both healthcare and to support clinical trials. ‘The technology generates real- time continuous data that can provide far greater insight into health and disease than, say, an ECG, blood test, or blood pressure measurement taken at periodic intervals in the physician’s office,’ commented Conkright. ‘This 24/7 monitoring is critical, given that many physiological parameters have a daily rhythm, and so deviations from the patient’s baseline may not be evident if only measured at certain times of day.’ The algorithms underpinning the physIQ


technology were originally developed at the US Department of Energy-funded Argonne National Laboratory, which is run by the University of Chicago, explained Conkright. ‘The AI technology was initially developed to detect anomalous behaviour


@scwmagazine | www.scientific-computing.com


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