Mathias Willadsen Brejnebøl1, Philip Hansen2, Janus Uhd Nybing3, Rikke Bachmann4, Ulrik Ratjen2, Ida Vibeke Hansen5, Anders Lenskjold3, Martin Axelsen6, Michael Lundemann6, Mikael Boesen3. 1. Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; Radiologic Artificial Intelligence Testcenter, Bispebjerg, Frederiksberg, Herlev and Gentofte Hospitals, Copenhagen, Denmark. Electronic address: mathiaswbrejne@outlook.com. 2. Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark. 3. Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; Radiologic Artificial Intelligence Testcenter, Bispebjerg, Frederiksberg, Herlev and Gentofte Hospitals, Copenhagen, Denmark. 4. Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; Radiography, Department of Technology, University College Copenhagen, Denmark. 5. Department of Radiology, Herlev and Gentofte Hospital, Copenhagen, Denmark. 6. Radiobotics ApS, Copenhagen, Denmark.
Abstract
PURPOSE: To externally validate an artificial intelligence (AI) tool for radiographic knee osteoarthritis severity classification on a clinical dataset. METHOD: This 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. RESULTS: 50 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). CONCLUSIONS: The 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.
PURPOSE: To externally validate an artificial intelligence (AI) tool for radiographic knee osteoarthritis severity classification on a clinical dataset. METHOD: This 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. RESULTS: 50 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). CONCLUSIONS: The 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.