External validation of an artificial intelligence tool for radiographic knee osteoarthritis severity classification

Abstract

PurposeTo externally validate an artificial intelligence (AI) tool for radiographic knee osteoarthritis severity classification on a clinical dataset. MethodThis retrospective, consecutive patient sample, external validation study used weight-bearing, non-fixed-flexion posterior-anterior knee radiographs from a clinical production PACS. The index test was ordinal Kellgren-Lawrence grading by an AI tool, two musculoskeletal radiology consultants, two reporting technologists, and two resident radiologists. Grading was repeated by all readers after at least four weeks. Reference test was the consensus of the two consultants. The primary outcome was quadratic weighted kappa. Secondary outcomes were ordinal weighted accuracy, multiclass accuracy and F1-score. Results50 consecutive patients between September 24, 2019 and October 22, 2019 were retrospectively included (3 excluded) totaling 99 knees (1 excluded). Quadratic weighted kappa for the AI tool and the consultant consensus was 0.88 CI95% (0.82–0.92). Agreement between the consultants was 0.89 CI95% (0.85–0.93). Intra-rater agreements for the consultants were 0.96 CI95% (0.94–0.98) and 0.94 CI95% (0.91–0.96) respectively. For the AI tool it was 1 CI95% (1–1). For the AI tool, ordinal weighted accuracy was 97.8% CI95% (96.9–98.6 %). Average multiclass accuracy and F1-score were 84% (83/99) CI95% (77–91%) and 0.67 CI95% (0.51–0.81). ConclusionsThe AI tool achieved the same good-to-excellent agreement with the radiology consultant consensus for radiographic knee osteoarthritis severity classification as the consultants did with each other.