Publications by authors named "R Kijowski"
Purpose: Accurately predicting the expected duration of time until total knee replacement (time-to-TKR) is crucial for patient management and health care planning. Predicting when surgery may be needed, especially within shorter windows like 3 years, allows clinicians to plan timely interventions and health care systems to allocate resources more effectively. Existing models lack the precision for such time-based predictions.
View Article and Find Full Text PDFFemoroacetabular impingement (FAI) is a cause of hip pain and can lead to hip osteoarthritis. Radiological measurements obtained from radiographs or magnetic resonance imaging (MRI) are normally used for FAI diagnosis, but they require time-consuming manual interaction, which limits accuracy and reproducibility. This study compares standard radiologic measurements against radiomics features automatically extracted from MRI for the identification of FAI patients versus healthy subjects.
View Article and Find Full Text PDFArticle Synopsis
- Different hip pathologies result from abnormal shapes in bone structures like the femur and acetabulum, which can be diagnosed using 3D models derived from MR images.
- Deep learning techniques can streamline the segmentation of these models, but their effectiveness hinges on the quality and size of training data, which can be enhanced through data augmentation and transfer learning.
- This study found that data augmentation outperformed transfer learning in automatically segmenting hip structures, achieving higher accuracy and better similarity scores compared to traditional manual methods.
Article Synopsis
- Hamstring strain injuries (HSI) are common in athletes and often lead to reinjury, with MRI showing residual injury not always correlating with strength deficits or reinjury risks.
- This study explored the potential of diffusion tensor imaging (DTI) to assess muscle microstructure changes and predict clinical outcomes like strength and reinjury rates after HSI.
- Findings showed a significant association between differences in eccentric strength and specific DTI metrics, suggesting that DTI could better reveal microstructural changes linked to strength than traditional MRI methods.
This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI).
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