We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




AI-Based Algorithm Enables Quicker HF Diagnosis

By HospiMedica International staff writers
Posted on 01 Nov 2021
Print article
Image: An AI-based tool can identify heart failure from ECG (Photo courtesy of MSSM/ JACC)
Image: An AI-based tool can identify heart failure from ECG (Photo courtesy of MSSM/ JACC)
A novel deep learning (DL) computer algorithm can identify subtle electrocardiogram (ECG) shifts that predict heart failure (HF), according to a new study.

The DL algorithm, developed at the Icahn School of Medicine at Mount Sinai (MSSM; New York, NY, USA), is designed to quantify left ventricular (LV) and right- ventricular (RV) dysfunction from ECG data in order to assist diagnostic workflow. To do so, a computer read 147,636 patient reports paired to 715,890 ECGs; the written reports acted as a standard set of clinical data so that the computer could compare them with identified patterns in the ECG images, helping the DL algorithm learn how to recognize heart pumping strengths via LV ejection fraction (LVEF).

The results showed the algorithm was 94% accurate at predicting which patients had a healthy LVEF and 87% accurate at predicting those who had an LV ejection fraction that was below 40%. The AI algorithm, however, was only 73% accurate at predicting HF in patients with an LVEF between 40% and 50%. The results also suggested that it was 84% accurate in detecting RV weaknesses, defined by more descriptive terms extracted from the ECG reports. The study was published on October 13, 2021, in JACC: Cardiovascular Imaging.

“We showed that deep-learning algorithms can recognize blood pumping problems on both sides of the heart from ECG waveform data. Ordinarily, diagnosing these type of heart conditions requires expensive and time-consuming procedures,” said senior author Benjamin Glicksberg, PhD, of the Hasso Plattner Institute for Digital Health at MSSM. “We are in the process of carefully designing prospective trials to test out its effectiveness in a more real-world setting. We hope that this algorithm will enable quicker diagnosis of heart failure.”

Clinicians currently rely on ECGs as a diagnostic tool, analyzing data points with the naked eye in order to identify patterns and abnormalities that are characteristic of heart disease. The problem is this takes time, and is entirely reliant on training and expertise. An experienced clinician can be reasonably reliable, but expertise levels vary significantly. Currently, AI tools to estimate cardiac function are restricted to quantification of very low LVEF values, when clinical HF is already evident.

Related Links:
Icahn School of Medicine at Mount Sinai

Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
Infant Blood Draw Station
Infant Blood Draw Station

Print article

Channels

Surgical Techniques

view channel
Image: NTT and Olympus have begun the world\'s first joint demonstration experiment of a cloud endoscopy system (Photo courtesy of Olympus)

Cloud Endoscopy System Enables Real-Time Image Processing on the Cloud

Endoscopes, which are flexible tubes inserted into the body's natural openings for internal examination and biopsy collection, are becoming increasingly vital in medical diagnostics. Their minimal invasiveness... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The PATHFAST hs-cTnI-II high-sensitivity troponin assay has been developed for the PATHFAST Biomarker Analyzer (Photo courtesy of Polymedco)

POC Myocardial Infarction Test Delivers Results in 17 Minutes

Chest pain is the second leading cause of emergency department (ED) visits by adults in the United States, generating over 7 million visits annually. In the event of a suspected heart attack, physicians... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.