Artificial intelligence in osteoarthritis: Repair by knee joint distraction shows association of pain, radiographic and immunological outcomes

Abstract

Objectives: Knee joint distraction (KJD) has been associated with clinical and structural improvement and SF marker changes. The current objective was to analyse radiographic changes after KJD using an automatic artificial intelligence-based measurement method and relate these to clinical outcome and SF markers. Methods: Twenty knee osteoarthritis patients were treated with KJD in regular care. Radiographs and WOMAC were collected before and ∼1 year post-Treatment. SF was aspirated before, during and after treatment; biomarker levels were assessed by immunoassay. Radiographs were analysed to obtain compartmental minimum and standardized joint space width (JSW), Kellgren-Lawrence (KL) grades, compartmental joint space narrowing (JSN) scores, and osteophytosis and sclerosis scores. Results were analysed for the most affected compartment (MAC) and least affected compartment. Radiographic changes were analysed using the Wilcoxon signed rank test for categorical and paired t-Test for continuous variables. Linear regression was used to calculate associations between changes in JSW, WOMAC pain and SF markers. Results: Sixteen patients could be evaluated. JSW, KL and JSN improved in around half of the patients, significant only for MAC JSW (P < 0.05). MAC JSW change was positively associated with WOMAC pain change (P < 0.04). Greater monocyte chemoattractant protein 1 (MCP-1) and lower TGFβ-1 increases were significantly associated with changes in MAC JSW (P < 0.05). MCP-1 changes were positively associated with WOMAC pain changes (P < 0.05). 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.