Imagine your heart's electrical system has a glitch caused by a typo in your genetic code, but doctors aren't sure if that typo is actually dangerous. This work uses AI and tiny lab-grown heart tissues to figure out which genetic glitches cause heart failure. It's like using a simulator to test a car part before putting it in a real engine to see if it will break.
Clinicians cannot currently treat Arrhythmogenic cardiomyopathy (ACM) beyond preventing sudden death because they cannot determine if specific genetic mutations (VUS) are actually causing the disease.
An AI-driven analysis system combined with 3D cardiac microtissues and murine models to map genetic mutations to physical heart defects.
If you are a drug discovery firm dealing with the lack of targets for rare cardiac diseases — this project developed a way to link genetic mutations to physical heart changes that identifies new targets for therapies. This helps move from palliative care to curative treatments.
If you are a genetic testing laboratory dealing with 'variants of uncertain significance' that confuse clinicians — this project developed AI-driven analyses of high throughput screening to clarify if a mutation is harmful. This increases the clinical utility of genetic tests.
If you are a bio-printing company dealing with the need for more accurate disease models — this project developed cardiac microtissues to study the genotype-phenotype relationship. This provides a validated method for testing how specific mutations affect heart tissue.
Based on available project data, no pricing or cost structures have been disclosed as the project is in the research phase.
The project utilizes high throughput screening and AI, which are inherently scalable, though current work focuses on establishing the genotype-phenotype relationship.
Based on available project data, specific patent or licensing details are not provided; it is currently a funded EU research project.
It aims to combine clinical data with genetic and molecular information using AI to help doctors interpret uncertain genetic variants.
The project period is from 2023-10-01 to 2026-09-30, suggesting that results will be finalized by late 2026.
The consortium consists of 8 partners across 4 countries, showing a balanced mix of 3 universities, 3 research institutes, and 2 industry partners. With a 25% industry ratio, the project has a moderate bridge to commercialization, though the lack of SMEs suggests it is currently driven by large-scale academic and research entities.
Contact Universita Degli Studi di Padova
Contact us to explore licensing opportunities for AI-driven cardiac screening.
