AI Analytics Driving Orthopaedic Excellence
As we enter August, the Akunah team is proud to share the latest in innovation, research, and collaboration across orthopaedics and medical AI. This month highlights a trio of exciting developments: global presentations, regional events, and groundbreaking publications that push forward the frontiers of personalised care.
Global Stage: NZSES & IOM 2025
NZSES 2025
We joined the New Zealand Shoulder and Elbow Society (NZSES) Meeting to share how Reflect, our AI-powered surgical planning tool, is helping shoulder surgeons plan with greater precision. Thanks to Dr. Marc Hirner and Dr. Ryan Gao for hosting a standout event.
International Osteotomy Meeting
At IOM, we showcased Insight, our real-time AI analytics engine built for personalised surgery, registry integration, and patient-specific decision-making. A big thank you to Dr. David Parker, Dr. Al Getgood, and Dr. Brett Fritsch for leading such an innovative program
Research Spotlight: Automating Fatty Infiltration Classification on MRI
We’re proud to share our latest publication with our QUASR collaborators, featured in the Journal of Shoulder and Elbow Surgery (July 2025).
Congratulations to lead author Dr Asma Salhi, PhD, and Akunah team members Dr Kristine Italia, MD, FPOA, Dr Ignacio Viedma, PhD, Dr Jashint Maharaj, MBBS, FRSPH, and Prof Ashish Gupta, MBBS, MSc, FRACS, FAOrthoA for their contributions, alongside our esteemed co-authors.
This study introduces a deep learning framework that classifies rotator cuff fatty infiltration on MRI scans with expert-level accuracy — streamlining a process that has traditionally been subjective and time-consuming.
Key Highlights:
· Simplified 3-grade system based on Goutallier classification
· 383 patients, 1,149 MRI images analysed
· Best model: 91.1% accuracy, 95.5% specificity, ICC 0.91
· AI visualisation confirms clinical relevance
Why it matters:
By combining in-domain transfer learning, feature fusion, and machine learning classifiers, this approach delivers accurate, robust, and expert-consistent classification of fatty infiltration on MRI. Automating this process reduces subjectivity, saves time, and offers a scalable solution that can be integrated into daily clinical practice — supporting more consistent decision-making and enhancing surgical planning.
Looking Ahead
If you missed us at NZSES or IOM Meetings, don’t worry.
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