Overview
RheumaFinder is an AI-powered application that analyzes CT images for structural bone lesions of the sacroiliac joints, developed for opportunistic detection in CT studies performed for other clinical indications without requiring additional imaging. The cloud-native, PACS-agnostic software integrates as an add-on application to any PACS system to enable early detection of structural joint damage and improve patient outcomes.
Key people
In the news
- RheumaFinder is now ISO 13485:2016 certified. Our Quality Management System for the design, development and delivery of RFMaple, our AI software for opportunistic detection of structural lesions on routine CT, has been certified by Kiwa Dare. For us this is one step in a planned sequence, not an end point: ✅ Clinical evidence: multicenter evaluation in 2,262 patients ✅ ISO 13485: certified, with zero major non-conformities across Stage 1 and Stage 2 ⏳ MDR CE marking (Class IIa): technical documentation review in progress ➡️ Next:
- Last week, Maria Fonseca joined #BostonBiotechWeek and #RESI in Boston on behalf of Obulytix, connecting with investors, biotech entrepreneurs, researchers, and industry leaders from across the life sciences ecosystem. A particular highlight was sharing the experience with the Belgian biotech delegation and exchanging perspectives on the challenges and opportunities of translating innovation from bench to patient in our local ecosystem. At RESI, organized by Life Science Nation, we gained valuable insights into US and
- The province of East-Flanders has an amazing track record in supporting the Flamish ecosystem for innovation in #HealthTech. Thnx for featuring #RheumaFinder in this great article, explaining how the 6-up-to-10-year delay in diagnosing #rheumatic diseases causes massive, irreversible long-term disability and human suffering. By using #AI, RheumaFinder is preventing chronic disease progression and relieving severe future strain on public healthcare systems, helping unaware patients and bringing smart #innovation to each
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