Imagine a smart assistant for heart doctors that automatically measures and reads ultrasound images. Instead of a doctor spending an hour manually clicking and measuring a heart, the AI does the heavy lifting in seconds. It's like moving from a hand-drawn map to a GPS that instantly tells you exactly where you are and what the road looks like.
Heart ultrasound interpretation is a bottleneck in cardiology due to high expertise requirements and time-consuming manual measurements. This leads to 30% inaccuracy rates and patient wait times of 4-5 weeks.
An AI-driven software tool that automates the classification of heart image views, detection of heart cycle phases, and the generation of standardized reports.
If you are a clinic dealing with 4-5 week patient waiting times — this project developed an AI-driven tool that automates heart ultrasound analysis. It reduces the 50-85% of test time currently spent on manual measurements, allowing for faster patient throughput.
If you are a software provider dealing with fragmented diagnostic workflows — this project developed a tool that seamlessly integrates with existing hospital networks. It allows analyzed reports to be accessible on any workstation moments after images are uploaded.
If you are a manufacturer dealing with the growing availability of cheap, small ultrasound devices that lack expert users — this project developed deep learning networks to standardize reporting. This ensures quality diagnosis even when the operator lacks high-level expertise.
Based on available project data, specific pricing or licensing costs are not disclosed.
The tool is designed to integrate with existing hospital network infrastructure, making it scalable across various workstations within a healthcare system.
Based on available project data, the project is coordinated by LIGENCE UAB, but specific IP or licensing terms are not provided.
Yes, it follows standardized protocols curated by cardiologist associations such as the European Society of Cardiology (ESC) and American Heart Association (AHA).
It integrates with the hospital's network so that results are accessible on any workstation moments after images are uploaded to the server.
The project is led by a single SME, LIGENCE UAB from Lithuania, with a 100% industry ratio. This lean structure suggests a highly focused commercial drive and a direct path to market without the typical delays of academic-industrial coordination.
Contact LIGENCE UAB in Lithuania for partnership opportunities.
Contact us to explore integration of this AI tool into your cardiology workflow.
