Cardiology/Vascular Diseases


Video from RSNA 2019: Dr. Paul Chang on AI and radiology

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Learning to perform and interpret radiographs is "hard". Learning to perform and interpret barium studies is "hard". Learning to perform and interpret sonograms is "hard". Learning to perform and interpret pneumoencephalograms was "hard". Learning to perform and interpret angiograms is "hard".


Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression

Neural Information Processing Systems

This paper develops a prediction-based prescriptive model for optimal decision making that (i) predicts the outcome under each action using a robust nonlinear model, and (ii) adopts a randomized prescriptive policy determined by the predicted outcomes. The predictive model combines a new regularized regression technique, which was developed using Distributionally Robust Optimization (DRO) with an ambiguity set constructed from the Wasserstein metric, with the K-Nearest Neighbors (K-NN) regression, which helps to capture the nonlinearity embedded in the data. We show theoretical results that guarantee the out-of-sample performance of the predictive model, and prove the optimality of the randomized policy in terms of the expected true future outcome. We demonstrate the proposed methodology on a hypertension dataset, showing that our prescribed treatment leads to a larger reduction in the systolic blood pressure compared to a series of alternatives. A clinically meaningful threshold level used to activate the randomized policy is also derived under a sub-Gaussian assumption on the predicted outcome.


How bathroom scales could monitor heart failure - Futurity

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You are free to share this article under the Attribution 4.0 International license. An experimental device uses machine learning tools--and a bathroom scale--to monitor heart failure. Researchers envision this scenario: The user steps onto a the scale and touches metal pads. The device records an electrocardiogram from their fingers and--more importantly--circulation pulsing that makes the body subtly bob up and down on the scale. Machine learning tools compute that heart failure symptoms have worsened.


BioSig Technologies Announces New Collaboration on Development of Artificial Intelligence Solutions in Healthcare :: BioSig Technologies, Inc. (BSGM)

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BioSig Technologies, Inc. (NASDAQ: BSGM) ("BioSig" or the "Company"), a medical technology company developing a proprietary biomedical signal processing platform designed to improve signal fidelity and uncover the full range of ECG and intra-cardiac signals, today announced that the Company entered into a technical collaboration with Reified Capital, a provider of advanced artificial intelligence-focused technical advisory services to the private sector. Reified was co-founded by Dr. Alexander D. Wissner-Gross and Timothy M. Sullivan, the founders of Gemedy. The new collaboration with Cambridge, Massachusetts-based Reified will focus on developing a foundational artificial intelligence platform on the basis of integrated healthcare datasets, beginning with ECG and EEG data acquired by BioSig's first product, PURE EP(tm) System - a novel real-time signal processing platform engineered to provide electrophysiologists with high fidelity cardiac signals. Electrophysiology focused technological solutions developed under the terms of this collaboration will be integrated into the PURE EP(tm) technology platform. Reified is led by Harvard- and MIT-trained computer scientist and physicist Dr. Wissner-Gross, an award-winning computer scientist, physicist, entrepreneur and author.


Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

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The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia.


AI Accurately Detects Heart Failure From One Heartbeat

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Using a new artificial intelligence driven neural network doctors may be able to detect heart failure from a single heartbeat with 100% accuracy in the future according to a recent study published in Biomedical Signal Processing and Control Journal.



Artificial Intelligence Detects Signs of Heart Disease on Lung Cancer Screenings - Docwire News

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The use of artificial intelligence (AI) can provide an automated and accurate tool to measure a common marker of heart disease in patients undergoing lung cancer screening, according to a study presented today at the annual meeting of the Radiological Society of North America (RSNA). "The new cholesterol guidelines encourage using the calcium score to help physicians and patients decide whether to take a statin," said study co-senior author Michael T. Lu, M.D., M.P.H., director of AI in the Cardiovascular Imaging Research Center (CIRC) at Massachusetts General Hospital (MGH) in Boston in a press release about the findings. "For select patients at intermediate risk of heart disease, if the calcium score is 0, statin can be deferred. If the calcium score is high, then those patients should be on a statin." In this study, researchers trained a deep-learning system on cardiac CTs and chest CTs in which the coronary artery calcium had been measured manually.


CHI Health now using artificial intelligence to diagnose stroke patients

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Before 2019, Doctors at CHI Health had to wait up to three hours before diagnosing stroke victims. But thanks to artificial intelligence, they can now do that within just six minutes. On Aug. 28, Natalie Carr was getting ready for bed when she realized something wasn't right. "I remember sitting there and looking down at my hand. It felt like it was falling asleep," Carr said.


Artificial intelligence tool predicts life expectancy in heart failure patients - When Avi Yagil PhD Distinguished Professor of Physics at University of California San Diego flew home from Europe in 2012 he thought he had caught a cold from his tr...

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When Avi Yagil, PhD, Distinguished Professor of Physics at University of California San Diego flew home from Europe in 2012, he thought he had caught a cold from his travels. When a "collection of pills" did not improve his symptoms, his wife encouraged him to see a doctor. Further tests revealed something far more life-threatening to Yagil than the common cold. "A chest X-Ray showed my lungs were flooded with fluid, and a subsequent echocardiogram found I had damage to my heart." Yagil was diagnosed with heart failure.