Research Post
Objective
Preliminary assessment, via OMERACT filter, of manual and automated MRI hip effusion Volumetric Quantitative Measurement (VQM).
Methods
For 358 hips (93 osteoarthritis subjects, bilateral, 2 time points), 2 radiologists performed manual VQM using custom Matlab software. A Mask R-CNN artificial-intelligence (AI) tool was trained to automatically compute joint fluid volumes.
Results
Manual VQM had excellent inter-observer reliability (ICC 0.96). AI predicted hip fluid volumes with ICC 0.86 (status), 0.58 (change) vs. 2 human readers.
Conclusion
Hip joint fluid volumes are reliably assessed by VQM. It is feasible to automate this approach using AI, with promising initial reliability.
Feb 9th 2023
Research Post
Feb 6th 2023
Research Post
Read this research paper, co-authored by Fellow & Canada CIFAR AI Chair at Russ Greiner: Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms
Jul 7th 2022
Research Post
Read this research paper, co-authored by Fellow & Canada CIFAR AI Chair Russ Greiner: Prediction of Obsessive-Compulsive Disorder: Importance of neurobiology-aided feature design and cross-diagnosis transfer learning
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