AI-powered assessment of musculoskeletal health
Backed by a decade of research & top-tier peer-reviewed science
Intelligent MSK imaging
Advanced scoring with OARSI atlas and Kellgren-Lawrence system, plus quantitative anatomical measurements for precise joint health quantification.
AI Image Analysis
Automated scoring with Kellgren-Lawrence system and OARSI atlas
Multi-Joint Approach
Comprehensive body quantification for orthopedics and radiology
Vendor-Neutral
Seamless PACS integration with standard DICOM protocol
Diverse Applications
Surgery, emergency cases, clinical trials, and research
Cloud-Native Privacy
Google Cloud-powered deployment with security-first design
Science-Backed
Validated by leading institutions in several populations.
QMSK platform currently provides fully automatic analysis of knee radiographs, with new algorithms for additional modalities and joints in development. This software is not yet available as a medical device.
Peer-reviewed studies
> 1000 citations in top venues
Vaattovaara, E., Panfilov, E., Tiulpin, A., Niinimäki, T., Niinimäki, J., Saarakkala, S., & Nevalainen, M. T. (2025). Kellgren-Lawrence Grading of Knee Osteoarthritis using Deep Learning: Diagnostic Performance with External Dataset and Comparison with Four Readers. Osteoarthritis and Cartilage Open, 100580.
Nguyen, H. H., Blaschko, M. B., Saarakkala, S., & Tiulpin, A. (2023). Clinically-inspired multi-agent transformers for disease trajectory forecasting from multimodal data. IEEE transactions on medical imaging, 43(1), 529-541.
Nguyen, H. H., Saarakkala, S., Blaschko, M. B., & Tiulpin, A. (2020). Semixup: in-and out-of-manifold regularization for deep semi-supervised knee osteoarthritis severity grading from plain radiographs. IEEE Transactions on Medical Imaging, 39(12), 4346-4356.
Tiulpin, A., Melekhov, I., & Saarakkala, S. (2019). KNEEL: Knee anatomical landmark localization using hourglass networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (pp. 0-0).
Tiulpin, A., Klein, S., Bierma-Zeinstra, S. M., Thevenot, J., Rahtu, E., Meurs, J. V., ... & Saarakkala, S. (2019). Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data. Scientific reports, 9(1), 20038.
Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., & Saarakkala, S. (2018). Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Scientific reports, 8(1), 1727.