scholarly journals Comparison of Joint Space Width Determinations in Grade I and II Knee Osteoarthritis Patients Using Manual and Automatic Measurements

Author(s):  
Sugiyanto Sugiyanto
2016 ◽  
Vol 75 (Suppl 2) ◽  
pp. 871.1-871
Author(s):  
R. Ljuhar ◽  
S. Nehrer ◽  
B. Norman ◽  
D. Ljuhar ◽  
T. Haftner ◽  
...  

2021 ◽  
Author(s):  
James Chung Wai Cheung ◽  
Yiu Chow TAM ◽  
Lok Chun CHAN ◽  
Ping Keung CHAN ◽  
Chunyi WEN

Abstract Objectives To develop a deep convolutional neural network (CNN) for the segmentation of femur and tibia on plain x-ray radiographs, hence enabling an automated measurement of joint space width (JSW) to predict the severity and progression of knee osteoarthritis (KOA). Methods A CNN with ResU-Net architecture was developed for knee X-ray imaging segmentation. The efficiency was evaluated by the Intersection over Union (IoU) score by comparing the outputs with the annotated contour of the distal femur and proximal tibia. By leveraging imaging segmentation, the minimal and multiple JSWs in the tibiofemoral joint were estimated and then validated by radiologists’ measurements in the Osteoarthritis Initiative (OAI) dataset using Pearson correlation and Bland–Altman plot. The estimated JSWs were deployed to predict the radiographic severity and progression of KOA defined by Kellgren-Lawrence (KL) grades using the XGBoost model. The classification performance was assessed using F1 and area under receiver operating curve (AUC). Results The network has attained a segmentation efficiency of 98.9% IoU. Meanwhile, the agreement between the CNN-based estimation and radiologist’s measurement of minimal JSW reached 0.7801 (p < 0.0001). Moreover, the 32-point multiple JSW obtained the highest AUC score of 0.656 to classify KL-grade of KOA. Whereas the 64-point multiple JSWs achieved the best performance in predicting KOA progression defined by KL grade change within 48 months, with AUC of 0.621. The multiple JSWs outperform the commonly used minimum JSW with 0.587 AUC in KL-grade classification and 0.554 AUC in disease progression prediction. Conclusion Fine-grained characterization of joint space width of KOA yields comparable performance to the radiologist in assessing disease severity and progression. We provide a fully automated and efficient radiographic assessment tool for KOA.


2017 ◽  
Vol 63 (3) ◽  
pp. 125-128
Author(s):  
Octav Marius Russu ◽  
Andrei Marian Feier ◽  
Tudor Sorin Pop ◽  
Marcela Todoran ◽  
István Gergely

AbstractObjective: Our purpose was to assess the effect of a new hyaluronic acid-based (Hymovis®) injections on joint space width narrowing in patients diagnosed with knee osteoarthritis.Methods: A prospective clinical trial was conducted in the Department of Orthopedics and Traumatology II from the Clinical County Hospital, Tîrgu Mureș, Romania. Thirty-five patients diagnosed with idiopathic knee osteoarthritis received two intraarticular injections with hyaluronic acid-based hydrogel (24 mg of hyaluronic acid/3 ml) at one-week interval. Anteroposterior radiographs were obtained before the injections, at six and twelve months after. Minimum joint space width was measured by two senior orthopaedics surgeons at each follow up. Each radiograph was measured again by the same evaluators two weeks apart.Results: Thirty-one patients were present at the final follow-up. A minor reduction in mean weight was noticed (from 82.2 kg ± 16.2 kg to 80.9 kg ± 16.0, p > 0.398) without any correlation with joint space width narrowing. There were no major changes at the first follow up (6 months) regarding joint space narrowing. A reduction in joint space width was observed however at 12 months varying from 4.4 mm (SD ± 1.64, range 1.8-7.1) at the first assessment to 4.3 mm (SD ± 1.26, range 0.0-6.8) at the final follow-up but with no statistical difference (p=0.237).Conclusion: No significant modification in joint space width at the final follow-up secondarily proved that two injections of Hymovis® may slow down narrowing in the knee joint space over a one-year period.


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