A novel method for determining the Femoral-Tibial Angle of Knee Osteoarthritis on X-ray radiographs: data from the Osteoarthritis Initiative

Wahyuningrum, Rima Tri; Purnama, I Ketut Eddy; Verkerke, Gijsbertus Jacob; Ooijen, Peter M. A. van; Purnomo, Mauridhi Hery

doi:10.1016/j.heliyon.2020.e04433

Cited by 7 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Presymptomatic knee OA diagnosis also remains a big challenge as the radiographic pattern at early OA is insignificant and unnoticeable. Researchers have previously suggested a few advanced quantitative examination approaches, work by extracting diagnostic meaningful structural details [24], such as joint space width [25,26], cartilage thickness [27,28], meniscal thickness [29], and tibiofemoral angle [30], from various images. Although the proposed methods have demonstrated the quantification of joint structures correlated with osteoarthritic joint, the diagnostic precision is not validated.…”

Section: Potentials and Limitationsmentioning

confidence: 99%

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.

show abstract

Section: Potentials and Limitationsmentioning

confidence: 99%

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

Femorotibial angle scan–rescan reproducibility: A high‐precision calculation on a large cohort

Hall,

Jones,

van Arkel

2024

Knee surg. sports traumatol. arthrosc.

View full text Add to dashboard Cite

PurposeFemorotibial angle (FTA) is a convenient measure of coronal knee alignment that can be extracted from a short knee radiograph, avoiding the additional radiation exposure and specialist equipment required for full‐leg radiographs. While intra‐ and inter‐reader reproducibility from the same image has been reported, the full scan–rescan reproducibility across images, as calculated in this study, has not.MethodsIn this study, 4589 FTA measurement pairs from 2586 subjects acquired a year apart were used to estimate FTA scan–rescan reproducibility using data from the Osteoarthritis Initiative. Subjects with radiographic progression of osteoarthritis or other conditions that may cause a change in coronal knee alignment were excluded. Measurement pairs were analysed using paired‐samples tests to detect differences and compared to symptomatic changes in Western Ontario and McMaster Universities Arthritis Index scores for joint pain, stiffness and physical function to detect correlations.ResultsThe 95% limit of agreement and the paired‐samples correlation were calculated with high precision to be [−1.76°, +1.78°] and 0.938, considerably worse than the corresponding figures for intra‐ and inter‐reader reproducibility, without relation to symptomatic or radiographic changes in knee condition. This error will weakly attenuate and values from their true values in correlative studies involving FTA. The realistic maximum value for is 87% and for Pearson's is 93%.ConclusionThe scan–rescan reproducibility in FTA is almost double the intra‐ and inter‐reader reliability from a single scan. At almost ±2° accuracy, FTA is inappropriate for surgical use, but it is sufficiently reproducible to produce good correlations in studies predicting disease incidence and progression.Level of EvidenceLevel II, retrospective study.

show abstract

A novel framework integrating ensemble transfer learning and Ant Colony Optimization for Knee Osteoarthritis severity classification

Malik,

Yasmin,

Iqbal

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

A novel method for determining the Femoral-Tibial Angle of Knee Osteoarthritis on X-ray radiographs: data from the Osteoarthritis Initiative

Cited by 7 publications

References 32 publications

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Femorotibial angle scan–rescan reproducibility: A high‐precision calculation on a large cohort

A novel framework integrating ensemble transfer learning and Ant Colony Optimization for Knee Osteoarthritis severity classification

Contact Info

Product

Resources

About