… The survey findings are reported outlining the main characteristics of the proposed learning
algorithms, the application domains, the data sources investigated and the quality of the …

… tests performed to identify osteoarthritis in the knee, most of … methodology for diagnosing
osteoarthritis in the knee in a more … this learning algorithm is on the error-correction learning rule…

… a machine learning system would enable such an early identification of individuals for whom
osteoarthritis knee structure will degrade rapidly. Data were from the Osteoarthritis Initiative, …

… of other previously established automated segmentation algorithms,13-15 which have …
learning algorithm to classify in vivo MRI of human articular cartilage for the development of OA…

… To develop an automated deep learning-based algorithm that jointly uses Posterior-Anterior
(PA) and Lateral (LAT) views of knee radiographs to assess knee osteoarthritis severity …

… learning models to determine which model is best to classify the severity of knee osteoarthritis
using … The image set was composed of radiographs of native knees, in anteroposterior and …

… A total of 2151 knee data points were collected for predicting the progression rate of OA
and 2492 knee data for predicting the fate of OA knee data. Data are presented as means and …

This paper explored the hidden biomedical information from knee magnetic resonance (MR)
images for osteoarthritis (OA) prediction. We have computed the cartilage damage index (…

… knee OA outcome is of importance for distinguishing between patients with slow-onset knee
OA and those with rapidly progressive knee OA. By … for knee OA that used a k-NN algorithm (…

… In this work, we used machine learning (ML) algorithms on a fairly large set of subjects
and features to develop advanced prediction models that provide high classification and …