Non-invasive, telehealth-enabled Heart Failure detection
Our AI powered heart failure detection tool fills a crucial gap in the health industry, offering a non-invasive and highly accurate solution for early detection, even in tele-health scenarios.
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What is CardioTherMetrics?
CTM is a heart failure detection solution that offers a non-invasive and highly accurate alternative for HF diagnostics based on a proprietary AI-powered thermographic image analysis method.
Main differenciators
Why the World needs CTM
Heart failure affects 6.5 million patients in the US alone, leading to significant healthcare challenges and costs.
High Cost of HF Patient Care
High Cost of HF Patient Care
The annual cost of caring for a patient with Heart Failure is almost $30,000 in the United States.
75% to 80% of the direct costs for HF are attributable to inpatient hospital stays.
Need for telemedicine Solutions
Need for telemedicine Solutions
Current diagnostic methods like BNP or catheterization are invasive and pose risks to patients, while needing consumables and costly equipment. This makes them not suited for Telemedicine in remote or unattended scenarios.
Growing Demand for HF Detection
Growing Demand for HF Detection
There is a growing demand for Heart Failure Detection tools, with a current estimated market size of $1 billion in the US and $4 billion globally.
Our AI solution can be implemented in various settings, including home use, exam rooms, critical care units (CCUs), fitness centers, and pharmacies.
Access to Quality Health Care Needs AI
Access to Quality Health Care Needs AI
The emergence of AI technology presents a game-changing opportunity in the field of healthcare, offering improved diagnostic capabilities and outcomes. Our proven machine learning techniques ensure safe, consistent and reliable outcomes with lower costs and accuracies that challenge the traditional methods.
Our process…
developing a ground-breaking medical technology.
Stage 1 - Thermography
Demonstrate the usefulness of thermography and the existence of thermal gradients in patients with heart disease.
European Respiratory Congress – Paris 2018
Stage 2 - Machine Learning
Demonstrate the utility of thermal gradients and machine learning to predict cardiac performance.
European Society of Cardiology – Tallin 2019
Stage 3 - Hemodynamics
Investigate the relationship between thermal gradients and hemodynamics measured by the Swan-Ganz catheter in patients with Cardiogenic Shock
Prospective Study (not published) – 2020/2021
Stage 4 - Clinical Tests
CardioThermetrics for the diagnosis of Acute Heart Failure in patients admitted to a Critical Care Unit VS patients without heart failure
2nd Prospective Study (not published) – 2022/2023
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