Analytics4Life

Publications

2025

Efficacy of a US-developed machine-learned coronary artery disease algorithm in China

Cardiac computed tomography angiography (CCTA), the premier coronary artery disease (CAD) rule-out test, is less available in rural areas (vs urban) in the US. To address this gap…

Facilitating Earlier Diagnosis of Pulmonary Hypertension Using a Novel Noninvasive Diagnostic

Pulmonary hypertension is frequently underdiagnosed due to limitations of transthoracic echocardiography. CorVista PH is a novel, Food and Drug Administration–cleared point-of-care diagnostic…

2024

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

The methodology to acquire the physiological signal for a Coronary Artery Disease (CAD) test is presented. A method is proposed to interpret the CAD score concerning test positivity and..

Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Functionally Significant Coronary Artery Disease

Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective…

Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm

Artificial intelligence, particularly machine learning, has gained prominence in medical research due to its potential to develop non-invasive diagnostics. Pulmonary hypertension presents a diagnostic challenge due…

Development of a Non-Invasive Machine-Learned Point-of-Care Rule-Out Test for Coronary Artery Disease

The current standard of care for coronary artery disease (CAD) requires an intake of radioactive or contrast enhancement dyes, radiation exposure, and stress and may take days to weeks…

2023

Machine Learning to Detect Pulmonary Hypertension at Point-of-Care

Pulmonary hypertension (PH) is defined by mean pulmonary arterial pressure (mPAP) of ≥21 mmHg or ≥25 mmHg by right heart catheterization (RHC), (2022 and 2015 ERS/ESC guidelines). Based…

A Supervised Machine-learned Algorithm to IDENTIFY PH in Patients With New Onset Symptoms

This supervised machine-learned model provides strong preliminary evidence that an algorithm with performance superior to TTE can be developed to assess the likelihood of PH in patients with new onset

Selection of Appropriate Validation Populations for Cardiology Research—Be Careful!

Machine learning (ML) and its applications have become an important part of everyday life as well as medical practice, from retinal scans for airport access to cancer detection in pathological specimens. Eloquent

2022

Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care

Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures…

Development and validation of a machine learned algorithm to IDENTIFY functionally significant coronary artery disease

Multiple trials have demonstrated broad performance ranges for tests attempting to detect coronary artery disease. The most common test, SPECT, requires capital-intensive equipment, the use of radionuclides, induction of…

Identifying Novel Phenotypes of Elevated Left Ventricular End Diastolic Pressure Using Hierarchical Clustering of Features Derived From Electromagnetic Waveform Data

Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated…

2021 – 2020

Predicting cardiac disease from interactions of simultaneously-acquired hemodynamic and cardiac signals

Coronary artery disease (CAD) and heart failure are the most common cardiovascular diseases. Non-invasive diagnostic testing for CAD requires radiation, heart rate acceleration, and imaging…

Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence

The bridge of artificial intelligence to cardiovascular medicine has opened up new avenues for novel diagnostics that may significantly enhance the cardiology care pathway. Cardiac phase space…

Machine learning: at the heart of failure diagnosis

This article is an overview of recent applications of ML to achieve improved diagnosis of HF and the resultant implications for patient management…

First-in-Man Development of a Machine Learning Cardiac Phase Space Analytic Approach to Predict Elevated Left Ventricular Pressures

Phase space is a mechanical systems approach and biopotential data representation of an object in 3D space. This study aimed to develop and validate a machine-learned cardiac phase space analytic (cPSA) approach to predict LVEDP…

2019 – 2013

Machine-Learned Algorithms Utilizing Novel Tomography for Evaluating Coronary Artery Disease

Machine learning utilizing convolutional neural networks and elastic net combined with a novel cardiac phase space tomography (cPST) now permits rapid analysis of thoracic phase signals for the…

Diagnostic Accuracy of Machine Learned Algorithms Utilizing a Novel Form of Cardiac Phase Tomography (cPST) versus Single Photon Emission Tomography (SPECT) in the Assessment of CAD

Machine learning through convolutional neural networks and elastic net combined with a novel form of cardiac phase space tomography (cPST) now permits rapid analysis of thoracic phase signals…

Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning

Artificial intelligence (AI) techniques are increasingly applied to cardiovascular (CV) medicine in arenas ranging from genomics to cardiac imaging analysis. Cardiac Phase Space Tomography Analysis (cPSTA)…

Noninvasive Detection of Coronary Artery Disease Using Resting Phase Signals and Advanced Machine Learning

Artificial intelligence (AI) techniques are increasingly being applied to cardiovascular (CV) medicine, yielding diagnostic tools that may significantly enhance the care of cardiac patients.

Assessing Coronary Artery Disease by Cardiac Phase Tomography Using Machine-Learned Algorithms in Obese and Elderly Subject

Combined with machine learning, cardiac phase space tomography analysis (cPSTA) evaluates thoracic physiological signals, without the use of radiation, exercise, or pharmacological stress…

Gender based Assessment of Coronary Artery Disease by Cardiac Phase Tomography Using Machine-Learned Algorithms

Machine-learned solutions are rapidly being implemented for the analysis of health care system based big data. Cardiac phase space tomography analysis (cPSTA) analyzes thoracic physiological signals…

Coronary Artery Disease Learning and Algorithm Development Study: Early Analysis of Ejection Fraction Evaluation

Heart failure is a progressive disease affecting approximately 6 million people in the United States. Left ventricular ejection fraction (LVEF) is used to guide therapy and determine cardiac risks…

Reliable Estimation of Left Ventricular Ejection Fraction Using High Resolution Surface Electrocardiography

Estimation of left ventricular (LV) systolic function is central to the prescription of medical and device therapy. Yet, significant variability in LV ejection fraction (EF) values exist among the methods used. Of these…