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Osteoarthritis, Knee

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Prosthetic orientation, limb alignment, and soft tissue balance with bi-cruciate stabilized total knee arthroplasty: a comparison between the handheld robot and conventional techniques.

International orthopaedics
PURPOSE: This study aimed to examine the prosthetic orientations, limb alignment, intraoperative soft tissue balance, and early clinical outcomes associated with the use of the relatively new handheld robot technique compared to those associated with...

Deep Learning Phenotype Automation and Cohort Analyses of 1,946 Knees Using the Coronal Plane Alignment of the Knee Classification.

The Journal of arthroplasty
BACKGROUND: The Coronal Plane Alignment of the Knee (CPAK) classification allows for knee phenotyping which can be used in preoperative planning prior to total knee arthroplasty. We used deep learning (DL) to automate knee phenotyping and analyzed CP...

Rehabilitation approach in robot assisted total knee arthroplasty: an observational study.

BMC musculoskeletal disorders
BACKGROUND: The purpose of this study is to evaluate the impact of total knee arthroplasty (TKA) with the aid of Navio Robot, comparing it with standard prosthetic surgery on the functional outcomes of patients after an intensive rehabilitation progr...

Cost-Effectiveness of Robot-Assisted Total Knee Arthroplasty: A Markov Decision Analysis.

The Journal of arthroplasty
BACKGROUND: Robot-assisted total knee arthroplasty (rTKA) may improve clinical outcomes for patients who have end-stage osteoarthritis of the knee. However, the costs of rTKA are high, and there is a paucity of data evaluating the cost-effectiveness ...

Deep learning applications in osteoarthritis imaging.

Skeletal radiology
Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartila...

Prediction of knee adduction moment using innovative instrumented insole and deep learning neural networks in healthy female individuals.

The Knee
BACKGROUND: The knee adduction moment, a biomechanical risk factor of knee osteoarthritis, is typically measured in a gait laboratory with expensive equipment and inverse dynamics modeling software. We aimed to develop a framework for a portable knee...

Accuracy of Advanced Active Robot for Total Knee Arthroplasty: A Cadaveric Study.

The journal of knee surgery
Although the accuracy of other types of robotic systems for total knee arthroplasty (TKA) has been assessed in cadaveric studies, no investigations have been performed to evaluate this newly advanced active robotic system. Therefore, the authors aime...

A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading.

Analytical methods : advancing methods and applications
Methods for automatic image analysis are demanded for dealing with the explosively increased imaging data in clinics. Osteoarthritis (OA) is a typical disease diagnosed based on X-ray imaging. Herein, we propose a novel modeling strategy based on YOL...

A More Posterior Tibial Tubercle (Decreased Sagittal Tibial Tubercle-Trochlear Groove Distance) Is Significantly Associated With Patellofemoral Joint Degenerative Cartilage Change: A Deep Learning Analysis.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To perform patellofemoral joint (PFJ) geometric measurements on knee magnetic resonance imaging scans and determine their relations with chondral lesions in a multicenter cohort using deep learning.

Automatic assessment of knee osteoarthritis severity in portable devices based on deep learning.

Journal of orthopaedic surgery and research
BACKGROUND: For knee osteoarthritis, the commonly used radiology severity criteria Kellgren-Lawrence lead to variability among surgeons. Most existing diagnosis models require preprocessed radiographs and specific equipment.