Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.
Compressed sensing in MRI enables high subsampling factors while maintaining diagnostic image quality. This technique enables shortened scan durations and/or improved image resolution. Further, compressed sensing can increase the diagnostic information and value from each scan performed. Overall, compressed sensing has significant clinical impact in improving the diagnostic quality and patient experience for imaging exams. However, a number of challenges exist when moving compressed sensing from research to the clinic. These challenges include hand-crafted image priors, sensitive tuning parameters, and long reconstruction times. Data-driven learning provides a solution to address these challenges. As a result, compressed sensing can have greater clinical impact. In this tutorial, we will review the compressed sensing formulation and outline steps needed to transform this formulation to a deep learning framework. Supplementary open source code in python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying data-driven compressed sensing in the clinical setting.
The Apple Watch is 97% accurate in detecting the most common abnormal heart rhythm, according to the findings of a new study conducted by researchers at the University of California, San Francisco, whose goal was to determine if the wearable could one day aid in stroke prevention. The UCSF Health eHeart study, which was conducted in large part via the Cardiogram app for iOS, examined 6,158 participants -- all of whom had normal echocardiogram (EKG) readings, with the exception of 200 individuals who had previously been diagnosed with paroxysmal atrial fibrillation. By implementing a self-developed, AI-based algorithm, researchers and biomedical engineers were then able to train a deep neural network to identify these abnormal heart rhythms utilizing data collected via the Apple Watch's heart rate monitor. While the overall eHeart study is more far-reaching, and examines a number of variables including heart rate, blood pressure, behavior, diet, genetics and more, the inherent portion of the Cardiogram-based inquiry was initiated back in 2016, with the primary intent of discovering whether or not the Apple Watch could detect an oncoming stroke. According to Cardiogram's co-founder and leading data scientist, Brandon Ballinger, approximately 25% of all strokes are caused by an abnormal heart rhythm such as the more commonly occurring abnormality, Atrial Fibrillation.
Apple Watch might be more than a fancy accessory for your wrist. The device could be of great help to heart patients, according to a'Health e-heart' study conducted by University of California, San Francisco, which finds that the device is 97 percent accurate in diagnosing irregular heartbeat. "Our results show that common wearable trackers like smartwatches present a novel opportunity to monitor, capture and prompt medical therapy for atrial fibrillation without any active effort from patients. While mobile technology screening won't replace more conventional monitoring methods, it has the potential to successfully screen those at an increased risk and lower the number of undiagnosed cases of AF," the report's senior author, Gregory M. Marcus, MD, MAS Endowed Professor of Atrial Fibrillation Research and Director of Clinical Research for the Division of Cardiology at UCSF, said in the findings published Wednesday. The research trained a deep neural network (DNN) and paired it with the Apple Watch and Cardiogram app.
The Apple Watch has been found to detect a heart condition that affects some 2.7 million people in the US, a new study has revealed. By pairing the smartwatch's heart rate sensors with artificial intelligence, researchers developed an algorithm capable of distinguishing an irregular heartbeat, known as atrial fibrillation, from a normal heart rhythm - and with 97 percent accuracy. Atrial fibrillation, although easily treatable, has been difficult to diagnose and the team believes their work could pave the way for new methods to identify the abnormality. The Apple Watch has been found to detect a heart condition that affects some 2.7 million people in the US, a new study has revealed. The algorithm was accurate 97 percent of the time using the smartwatch's heart rate sensor (stock) University of California, San Francisco, in collaboration with the app Cardiogram, trained a deep neural network with heart readings from 6,158 Cardiogram users.
The Apple Watch could be used to detect a heart condition that causes over 100,000 strokes every year, according to a new study. Heart health app Cardiogram and researchers from the University of California, San Francisco (UCSF) Cardiology Health eHeart project teamed up to take a closer look at just how effective the Watch can be at tracking the most clinically common heart abnormality, atrial fibrillation (AF). The irregularity, which is treatable but tough to diagnose using current medical standard practices, is the leading cause of heart failure. The mRhythm project that resulted from the pairing looked at the Apple Watch-sourced heart rate readings from 6,158 Cardiogram users. The data was then used to build an algorithm to detect the distinct heart rate variability pattern caused by AF.
According to a study conducted through heartbeat measurement app Cardiogram and the University of California San Francisco, the Apple Watch is 97 percent accurate in detecting the most common abnormal heart rhythm, when paired with an AI-based algorithm. The study involved 6,158 participants recruited through the Cardiogram app on Apple Watch. Most of the participants in the UCSF Health eHeart study had normal EKG readings. However, 200 of them had been diagnosed with paroxysmal atrial fibrillation (an abnormal heartbeat). Engineers then trained a deep neural network to identify these abnormal heart rhythms from Apple Watch heart rate data.