University of Oulu

Tiulpin, A., Saarakkala, S., Mathiessen, A., Hammer, H. B., Furnes, O., Nordsletten, L., Englund, M., & Magnusson, K. (2022). Predicting total knee arthroplasty from ultrasonography using machine learning. Osteoarthritis and Cartilage Open, 4(4), 100319.

Predicting total knee arthroplasty from ultrasonography using machine learning

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Author: Tiulpin, Aleksei1; Saarakkala, Simo1,2; Mathiessen, Alexander3,4;
Organizations: 1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
2Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
3Center for treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway
4Faculty of Medicine, University of Oslo, Oslo, Norway
5The Norwegian Arthroplasty Register, Department of Orthopaedic Surgery, Haukeland University Hospital, Bergen, Norway
6Department of Clinical Medicine, University of Bergen, Bergen, Norway
7Division of Orthopaedic Surgery, Oslo University Hospital, Oslo, Norway
8Clinical Epidemiology Unit, Orthopaedics, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
9Norwegian Institute of Public Health, Cluster for Health Services Research, Oslo, Norway
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
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Language: English
Published: Elsevier, 2022
Publish Date: 2023-05-25


Objective: To investigate the value of ultrasonographic data in predicting total knee replacement (TKR).

Design: Data from the Musculoskeletal Pain in Ullensaker study (MUST) was linked to the Norwegian Arthroplasty Register to form a 5–7 year prospective cohort study of 630 persons (69% women, mean (SD) age 64 (8.7) years). We examined the predictive power of ultrasound (US) features, i.e. osteophytes, meniscal extrusion, synovitis in the suprapatellar recess, femoral cartilage thickness, and quality for future knee osteoarthritis (OA) surgery. We investigated 4 main settings for multivariate predictive modeling: 1) clinical predictors (age, sex, body mass index, knee injury, familial OA and workload), 2) radiographic data (assessed by the Kellgren Lawrence grade, KL) with clinical predictors, 3) US features and clinical predictors. Finally, we also considered an ensemble of models 2) and 3) and used it as our fifth model. All models were compared using the Average Precision (AP) and the Area Under Receiver Operating Characteristic Curve (AUC) metrics.

Results: Clinical predictors yielded AP of 0.11 (95% confidence interval [CI] 0.05–0.23) and AUC of 0.69 (0.58–0.79). Clinical predictors with KL grade yielded AP of 0.20 (0.12–0.33) and AUC of 0.81 (0.67–0.90). The clinical variables with ultrasound yielded AP of 0.17 (0.08–0.30) and AUC of 0.79 (0.69–0.86).

Conclusions: Ultrasonographic examination of the knee may provide added value to basic clinical and demographic descriptors when predicting TKR. While it does not achieve the same predictive performance as radiography, it can provide additional value to the radiographic examination.

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Series: Osteoarthritis and cartilage open
ISSN: 2665-9131
ISSN-E: 2665-9131
ISSN-L: 2665-9131
Volume: 4
Issue: 4
Article number: 100319
DOI: 10.1016/j.ocarto.2022.100319
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
Funding: This study was funded by the Swedish Research Council (E0234801), the Greta and Johan Kock Foundation, the Swedish Rheumatism Association, the Österlund Foundation, Governmental Funding of Clinical Research within the National Health Service (ALF) and the Faculty of Medicine, Lund University, Sweden.
Copyright information: © 2022 Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International (OARSI). This is an open access article under the CC BY license (