Artificial intelligence in osteoarthritis: repair by knee joint distraction shows association of pain, radiographic and immunological outcomes
Authors' conclusion
Automatic radiographic measurements show improved joint structure in most patients after KJD in regular care. MAC JSW increased significantly and was associated with SF biomarker level changes and even with improvements in pain as experienced by these patients.
Study context
- Population
- Knee OA patients treated with knee joint distraction (KJD) in regular care16 evaluated
- Participants
- 20 patients
- Outcome
- Radiographic changes (JSW, KL grade, JSN, osteophytosis, sclerosis) by automatic AI-based measurement, related to WOMAC and SF markers
- Follow-up
- ∼1 year post-treatment
Methodological detailssingle-group, regular care, AI-based automatic radiographic measurement
- Single-group
- Regular care
- AI-based automatic radiographic measurement
Main finding
As reported in the abstract
Radiographic changes after KJD · ∼1 year
- Significant improvement only for most affected compartment (MAC) joint space width (JSW)
- P < 0.05
Observational data: associations, not effects.
Further findings
Association of MAC JSW change with WOMAC pain change
- MAC JSW change positively associated with WOMAC pain change
- P < 0.04
- Linear regression
Association of SF markers with MAC JSW change
- Greater MCP-1 and lower TGFβ-1 increases significantly associated with changes in MAC JSW
- P < 0.05
Association of MCP-1 change with WOMAC pain change
- MCP-1 changes positively associated with WOMAC pain changes
- P < 0.05
Original publication
Artificial intelligence in osteoarthritis: repair by knee joint distraction shows association of pain, radiographic and immunological outcomes. Rheumatology (Oxford). Published 29 December 2022. doi:10.
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