News and Traction
HEARTio announces three new advisors!
Pittsburgh - November 26, 2024
HEARTio is pleased to announce several well-renowned members to our clinical advisory board and our commercial advisory, Dr. Josh Brown is an emerging leader in emergency medicine innovation, Dr. Saurabh Dani is an expert in cardiovascular health economics application, and Nathan Qin is a globally renowned cardiovascular device innovator. We are excited to partner with all of them to bring HEARTio’s goals and dreams to fruition and to impact the lives of cardiovascular patients everywhere.
Dr. Joshua Brown, DO – Dr Brown graduated with a bachelor’s degree in economics from the University of Pittsburgh, before attending the Lake Erie College of Osteopathic Medicine. He completed his medical training at Jefferson and Rowan University where he served as the chief resident. He currently functions as the managing partner of Main Line Emergency Associates LLC, and functions as a practicing Emergency Medicine clinician at Bryn Mawr Hospital. A passionate advocate for medical technology, Dr. Brown manages a syndicate fund focused on health tech startups, and is currently leading the design of an innovative product aimed at improving outcomes in cardiac arrest care.
Dr. Saurabh Dani, MD – Dr. Dani is an experienced cardiologist with advanced training in non-invasive cardiology, echocardiography, lipid disorder management, and cardiac implantable rhythm device management. Dr. Dani is a healthcare services professional with a Master of Science from The London School of Economics and Political Science (LSE) and a graduate diploma in Clinical Epidemiology from McMaster University. He completed his medical education at King Edward Memorial Hospital Seth Gordhandas Sunderdas Medical College before completing his training at Saint Vincent Hospital in Worcester, MA and at the Lahey Hospital and Medical, Tufts University, Burlington, MA. He is currently an Associate Professor of Medicine at Tufts University School of Medicine.
Nathan Qin - The Founder & Managing Partner of qinnovativ medtech GmbH, Nathan is a globally connected healthcare innovation and business strategy expert in the USA, China, and the EU. Nathan received his Executive MBA at the Kellogg School of Management at Northwestern University in the USA and the Otto Beisheim School of Management in Germany. With a Masters in Industrial Design from the Umea Institute of Design in Sweden, Nathan has worked as a Senior Designer and Strategist at renowned MedTech companies such as Siemens Healthineers, Johnson & Johnson, and Boston Scientific. He has also held various business development positions in international consulting firms, successfully managing China-German background business projects.
HEARTio recognized on the Forbes 30U30 list!
New York City - January 1, 2024
HEARTio and its co-founders, Adam Butchy, Michael Leasure, and Utkars Jain , have been recognized on the Forbes 30 Under 30 list for 2024!
This achievement is a testament to the incredible journey HEARTio has had so far, and none of it would have been possible without the support of our amazing mentors, and the numerous opportunities that have come our way.
We are truly humbled by this recognition and want to express our deepest gratitude to everyone who has been a part of our journey. Your guidance and encouragement have played a pivotal role in our success. Thank you for believing in us and being a part of the HEARTio story.
Here is a link to the Forbes article, and another for one published on Technically.
HEARTio publishes an early look at reduced FFR detection
Pittsburgh - August 28, 2024
The current gold standard of coronary artery disease (CAD) diagnosis is invasive angiography, during which fractional flow reserve (FFR) measurement may be performed to confirm the clinical significance of a stenosis. The yield of routine and indiscriminate FFR in identifying hemodynamically significant stenoses is low.
To combat this, we have developed an artificial intelligence model - ECGio – designed to be deployed at the point of care to determine FFR through the analysis of a resting digital 12-lead electrocardiogram (ECG), a fast, real-time, cost-effective, widely accessible, and safe diagnostic method.
This study assessed the ability of ECGio to train, tune, and test itself through a cross-validation paradigm to predict the presence of a reduced FFR in the left anterior descending artery in a patient population presenting for invasive FFR.
The full pre-print is available here.
HEARTio’s second patent is issued!
Pittsburgh - July 31, 2024
We're thrilled to announce that HEARTio has been awarded our second U.S. patent! This significant milestone marks another step forward in our mission to revolutionize heart health diagnostics.
Our newly patented diagnostic tool leverages cutting-edge technology to provide accurate and timely assessments of heart conditions. The device incorporates a sensor that captures biosignals from the patient's heart, and a powerful computer system that utilizes a machine learning-based neural network.
The neural network is iteratively trained to identify patterns within the biosignals, enabling it to accurately predict the presence of various heart conditions. By processing the captured data through this sophisticated algorithm, the device can deliver valuable insights into a patient's cardiac health.
This groundbreaking patent further solidifies HEARTio's position as a leader in the field of heart health diagnostics. We are committed to continuing our research and development efforts to bring innovative solutions to market and improve the lives of people worldwide.
The full patent is available here.
HEARTio's Abstract Featured in Circulation
Pittsburgh - November 6, 2023
We are excited to announce that HEARTio's research has been published in the prestigious journal Circulation. Our abstract, titled "The Impact of Noise on Deep Learning-Based ECG Construction," explores the relationship between noise levels and the performance of deep learning models in constructing noise-free ECGs.
Our findings reveal that while deep learning models are not entirely immune to noise, they demonstrate remarkable resilience up to certain noise levels. This information is crucial for understanding the limitations and potential of deep learning models like ECGio in clinical practice.
We are committed to continuing our research to develop even more robust and effective tools for heart health diagnostics.
The full abstract is available here.
HEARTio’s Latest Research: Optimizing ECG Electrode Selection
Pittsburgh - May 25, 2023
HEARTio is thrilled to announce the publication of our latest research in the journal Biomedicines. This study, titled "Importance of Electrode Selection and Number in Reconstructing Standard Twelve Lead Electrocardiograms," explores the optimal electrode selection and number for accurately reconstructing a full twelve-lead ECG.
Electrocardiograms (ECGs) are essential tools for diagnosing heart conditions, but the number and placement of electrodes can significantly impact the quality and information obtained. While standard twelve-lead ECGs offer comprehensive cardiac assessments, newer devices with fewer electrodes often rely on algorithms to reconstruct a full twelve-lead signal. Our research aimed to determine the optimal electrode selection and number for accurately reconstructing standard twelve-lead ECGs. By analyzing data from 250 patients, we employed a deep learning model to assess the impact of different electrode combinations on reconstruction accuracy.
These results have significant implications for the development of more compact and cost-effective ECG devices. By carefully selecting electrodes and utilizing efficient reconstruction algorithms, manufacturers can optimize device performance while maintaining diagnostic accuracy.
Here is a link to the paper.
HEARTio Takes Home $75k at Tulane Business Model Competition
New Orleans - April 3, 2023
HEARTio is proud to announce its victory in the prestigious Tulane University Business Model Competition. Our innovative approach to using artificial intelligence for rapid and accurate heart abnormality detection earned us first place and a $75,000 cash prize.
Selected from a competitive field of 123 applicants, HEARTio's business model impressed judges with its potential to revolutionize emergency medical care. We extend our sincere gratitude to Tulane University, the Albert Lepage Center for Entrepreneurship and Innovation, and all involved in making this competition a success.
This win marks a significant milestone for HEARTio as we continue to develop our technology and bring it to market. We are excited to expand our reach and make a positive impact on the healthcare industry.
Read more about the competition here.
HEARTio's Latest Research: Uncovering Redundancy in ECG Leads
Pittsburgh - March 1, 2023
HEARTio is excited to announce the publication of our new research, "Redundancy and Novelty Between ECG Leads Based on Linear Correlation," in BIOSTEC’s Biosignals. This study delves into the relationships between the 12 leads of a standard ECG to identify redundant information and highlight unique contributions.
By analyzing ECG signals from the PTB-XL database and reconstructed signals generated by our deep learning model, ECGio, we employed the Pearson correlation coefficient to assess the linear correlations between leads. Our findings reveal that leads III, aVL, V1, and V2 exhibit lower correlations with other leads, indicating a greater degree of unique information.
This research has significant implications for ECG device design and data analysis. By understanding the redundancy between leads, we can optimize electrode selection and develop more efficient algorithms for ECG interpretation.
The full paper is available here.
HEARTio Achieves Major Milestone with First Patent
Pittsburgh - January 31, 2023
We HEARTio is thrilled to announce that our pioneering medical diagnostic tool has been granted a U.S. patent! The patent, titled "Medical diagnostic tool with neural model trained through machine learning for predicting coronary disease from ECG signals," was awarded on January 31, 2023.
This significant achievement marks a major milestone for HEARTio and validates the novelty and innovation of our solution. The patented technology leverages advanced machine learning algorithms to accurately predict coronary disease from ECG signals, empowering healthcare providers with valuable diagnostic insights.
What a way to start the year! As we continue to develop and refine our technology, this patent serves to validate the novelty and revolutionary nature of our technology.
The full patent is available here.
HEARTio Founders Recognized as University of Pittsburgh’s “Student Innovators of the Year”
Pittsburgh - April 22, 2022
HEARTio is proud to announce that our founders have been recognized as the University of Pittsburgh's "Student Innovators of the Year" for 2022! This prestigious award celebrates exceptional students at Pitt who have demonstrated entrepreneurial spirit and a commitment to making a positive impact on the world.
We are incredibly honored to receive this recognition, which reflects the hard work, dedication, and innovation of our entire team.
Read more about this here.
HEARTio Claims Victory at Baylor New Venture Competition!
Waco - March 30, 2022
HEARTio is thrilled to announce our first-place win and $50,000 cash prize at the Baylor New Venture Competition! Out of over 200 participating teams, HEARTio emerged as the top contender, showcasing our innovative use of AI for rapid and accurate heart abnormality detection.
We extend our sincere gratitude to Baylor University and all the coaches, mentors, judges, and volunteers who contributed to the competition's success. Their invaluable guidance and feedback played a crucial role in refining our narrative, messaging, and presentation.
This victory marks a significant milestone for HEARTio as we continue to develop our technology and bring it to market. We look forward to returning to the vibrant city of Waco, Texas, in the future.
Read more about it here.
HEARTio Pushes the Boundaries of ECG Reconstruction with Deep Learning
Pittsburgh - April 22, 2022
HEARTio takes a giant leap forward in ECG reconstruction with our latest research, "12-Lead ECG Reconstruction via Combinatoric Inclusion of Fewer Standard ECG Leads." Building on our deep learning model, ECGio, we've achieved breakthrough results in reconstructing full 12-lead signals from as little as a single lead.
This study demonstrates ECGio's ability to reconstruct a complete ECG with impressive accuracy using only lead II. Furthermore, by analyzing various lead combinations, we identified optimal setups for achieving high-fidelity reconstructions. These findings hold significant promise for the future of ECG technology. Imagine highly portable ECG devices with minimal leads, yet capable of providing accurate heart data thanks to ECGio's reconstruction power. This research also opens doors for exploring ECGio's robustness against noise and lead issues.
Read the full paper here.
Utkars Jain and Adam Butchy listed on Pittsburgh’s 30 Under 30 list for 2022
Pittsburgh - January 1, 2022
We're thrilled to announce that HEARTio's founders, Utkars Jain and Adam Butchy, have been honored as part of Pittsburgh's 30 Under 30 for 2022! This prestigious award, bestowed by the Pittsburgh Business Times, recognizes outstanding young professionals making significant contributions to the city's business community.
We are incredibly proud of Utkars and Adam's accomplishments and their dedication to developing innovative solutions for heart health. This recognition is a testament to their entrepreneurial spirit and the potential of HEARTio's technology to revolutionize cardiac care.
Photo Credit - Jim Harris of the Pittsburgh Business Times
Read more about it here.
HEARTio Publish an Abstract in Circulation
Pittsburgh - November 8, 2021
HEARTio is excited to announce the publication of our abstract, "Deep Learning Algorithm Predicts Angiographic Diameter Stenosis in Stable Patients Using Only a Standard 12-lead Electrocardiogram," in Circulation. This groundbreaking research demonstrates the potential of artificial intelligence to revolutionize the diagnosis of coronary artery disease (CAD).
Traditional ECG analysis methods have limitations in predicting the presence and extent of CAD, particularly in stable patients. Our deep learning algorithm, ECGio, addresses this challenge by accurately assessing the location and severity of coronary artery lesions using only a standard 12-lead ECG.
ECGio's ability to predict CAD from ECG data holds immense promise for early detection and improved patient outcomes. By leveraging AI, we can potentially identify at-risk individuals and initiate preventive measures before severe complications arise.
Read the abstract here.
HEARTio's AI Advances in Coronary Artery Disease Detection
Pittsburgh - November 1, 2021
HEARTio is proud to announce the publication of our abstract, "Deep Learning Algorithm Predicts Angiographic Diameter Stenosis in Stable Patients Using Only a Standard 12-lead Electrocardiogram," in the Journal of the American College of Cardiology for the Transcatheter Cardiovascular Therapeutics (TCT) conference.
This research showcases the potential of our artificial intelligence algorithm, ECGio, to accurately predict the presence of coronary artery disease (CAD) in stable patients. Our study demonstrated a high sensitivity and specificity in identifying significant CAD, highlighting the clinical value of ECGio in early detection and risk assessment.
Read the abstract here.
HEARTio Publishes Landmark Study on Over 1,600 Patients
Pittsburgh - April 22, 2022
HEARTio is proud to announce the publication of our groundbreaking research, "Deep Learning Algorithm Predicts Angiographic Coronary Artery Disease in Stable Patients Using Only a Standard 12-Lead Electrocardiogram," in the Canadian Journal of Cardiology, a prestigious peer-reviewed journal.
This study, featuring data from over 1600 patients, demonstrates the power of our artificial intelligence algorithm, ECGio, in accurately predicting the presence, location, and severity of coronary artery disease (CAD) using standard 12-lead ECGs.
Our findings highlight the potential of ECGio to revolutionize cardiac care by enabling early detection and risk assessment of CAD. This research is a significant step forward in leveraging AI for improved patient outcomes.
The full paper is available in both English and French.
The paper is available here.
