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Echoliv

The startup develops AI-driven software that assists radiologists and hepatologists in detecting liver cancer through ultrasound imaging. This technology enhances diagnostic accuracy and improves patient care by providing timely and precise identification of the disease.

Paris, France1300+ followers
Updated 2 months ago

Funding

$61K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Radiologists and hepatologists face challenges in accurately detecting liver cancer using traditional ultrasound imaging due to the subtle nature of early-stage tumors and the potential for human error. This can lead to delayed diagnoses and suboptimal patient outcomes.

Solution

The company offers an AI-powered software solution designed to improve the detection of liver cancer in ultrasound images. By leveraging advanced machine learning algorithms, the software analyzes ultrasound scans to identify potential cancerous lesions that may be missed by the human eye. This technology provides clinicians with a second opinion, enhancing diagnostic confidence and enabling earlier intervention. The software integrates seamlessly into existing clinical workflows, providing real-time analysis and decision support. Ultimately, this leads to more accurate diagnoses, improved patient care, and better overall outcomes in the fight against liver cancer.

Target Audience

The primary target audience includes radiologists, hepatologists, and other medical professionals involved in the diagnosis and treatment of liver cancer, as well as hospitals and imaging centers.

Features

  • AI-driven analysis of ultrasound images for liver cancer detection
  • Real-time decision support for radiologists and hepatologists
  • Integration with existing clinical ultrasound systems
  • Automated lesion detection and characterization
  • Quantitative analysis of tumor size and shape
  • Customizable reporting and visualization tools
  • Machine learning models trained on a large dataset of liver ultrasound images
This profile is AI-generated and may contain inaccuracies.