RheumaFinder is an AI-powered application that analyzes radiological images to detect early signs of rheumatic diseases, identifying hallmark lesions up to nine years before traditional diagnosis. By integrating with any PACS system, it enhances diagnostic accuracy and enables timely interventions, ultimately improving patient outcomes and reducing healthcare costs.
Funding
$300K 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.
Founders
Product
Problem
Rheumatic diseases often go undetected until irreversible joint damage occurs, leading to delayed treatment and reduced quality of life for patients. Incidental findings indicative of rheumatic diseases are frequently missed in standard medical images due to their subtle appearance and time constraints in clinical practice. Current diagnostic methods lack the sensitivity to identify early signs of structural damage, hindering proactive intervention.
Solution
RheumaFinder is an AI-powered software that integrates with Picture Archiving and Communication Systems (PACS) to detect early signs of rheumatic diseases in radiological images. The software analyzes images from various modalities, identifying hallmark lesions, including inflammatory joint damage, even before patients experience clinical symptoms. By acting as an extra layer of image interpretation, RheumaFinder enhances diagnostic accuracy and enables timely treatment, potentially years before traditional diagnosis. The cloud-native platform highlights AI-detected findings, providing an additional layer of support for radiologists and rheumatologists.
Target Audience
The primary target audience includes radiologists, rheumatologists, hospitals, and clinics seeking to improve early detection and diagnosis of rheumatic diseases.
Features
- AI-powered analysis of radiological images for early detection of rheumatic diseases
- Detection of hallmark lesions, including erosions and ankylosis, on CT scans
- Integration with existing PACS systems as an add-on application
- Identification of incidental findings indicative of rheumatic diseases
- Cloud-native architecture for scalability and accessibility
- Support for multiple imaging modalities
- Highlighted AI-detected findings for improved visualization
- Algorithms developed using high-quality, expert-labeled datasets