Abstract
Post-traumatic knee osteoarthritis is attributed to alterations in joint morphology, alignment, and biomechanics triggered by injury. While magnetic resonance (MR) imaging-based measures of joint morphology and alignment are relevant to understanding osteoarthritis risk, time consuming manual data extraction and measurement limit the number of outcomes that can be considered and deter widespread use. This paper describes the development and evaluation of a semi-automated software for measuring tibiofemoral and patellofemoral joint architecture using MR images from youth with and without a previous sport-related knee injury. After prompting users to identify and select key anatomical landmarks, the software can calculate 37 (14 tibiofemoral, 23 patellofemoral) relevant geometric features (morphology and alignment) based on established methods. To assess validity and reliability, 11 common geometric features were calculated from the knee MR images (proton density and proton density fat saturation sequences; 1.5 T) of 76 individuals with a 3-10-year history of youth sport-related knee injury and 76 uninjured controls. Spearman's or Pearson's correlation coefficients (95% CI) and Bland-Altman plots were used to assess the concurrent validity of the semi-automated software (novice rater) versus expert manual measurements, while intra-class correlation coefficients (ICC 2,1; 95%CI), standard error of measurement (95%CI), 95% minimal detectable change, and Bland-Altman plots were used to assess the inter-rater reliability of the semi-automated software (novice vs resident radiologist rater). Correlation coefficients ranged between 0.89 (0.84, 0.92; Lateral Trochlear Inclination) and 0.97 (0.96, 0.98; Patellar Tilt Angle). ICC estimates ranged between 0.79 (0.63, 0.88; Lateral Patellar Tilt Angle) and 0.98 (0.95, 0.99; Bisect Offset). Bland-Altman plots did not reveal systematic bias. These measurement properties estimates are equal, if not better than previously reported methods suggesting that this novel semi-automated software is an accurate, reliable, and efficient alternative method for measuring large numbers of geometric features of the tibiofemoral and patellofemoral joints from MR studies.
| Original language | English |
|---|---|
| Pages (from-to) | 1023-1035 |
| Number of pages | 13 |
| Journal | Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine |
| Volume | 236 |
| Issue number | 7 |
| Early online date | 5 May 2022 |
| DOIs | |
| Publication status | Published - Jul 2022 |
Bibliographical note
Funding Information:The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Alberta PrE-OA cohort is funded by the Canadian Institutes of Health Research (MOP 133597), the Alberta Team Osteoarthritis Team supported by Alberta Innovates, and the Alberta Children’s Hospital Research Institute Chair in Pediatric Rehabilitation (Alberta Children’s Hospital Foundation). The Sport Injury Prevention Research Centre (SIPRC) is one of ten International Olympic Committee Research Centers focused on Injury and Illness Prevention in Sport.
Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Alberta PrE-OA cohort is funded by the Canadian Institutes of Health Research (MOP 133597), the Alberta Team Osteoarthritis Team supported by Alberta Innovates, and the Alberta Children’s Hospital Research Institute Chair in Pediatric Rehabilitation (Alberta Children’s Hospital Foundation). The Sport Injury Prevention Research Centre (SIPRC) is one of ten International Olympic Committee Research Centers focused on Injury and Illness Prevention in Sport.
Publisher Copyright:
© IMechE 2022.