scholarly journals Multicentre Study Using Machine Learning Methods in Clinical Diagnosis of Knee Osteoarthritis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ke Zeng ◽  
Yingqi Hua ◽  
Jing Xu ◽  
Tao Zhang ◽  
Zhuoying Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Knee Osteoarthritis ◽  
Knee Joints ◽  
Imaging Data ◽  
Learning Methods ◽  
Remote Areas ◽  
Knee Oa ◽  
Machine Learning Methods ◽  
X Ray Imaging ◽  
Automatic Methods

Knee osteoarthritis (OA) is one of the most common musculoskeletal disorders. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from the subjectivity of doctors. In this study, we retrospectively compared five commonly used machine learning methods, especially the CNN network, to predict the real-world X-ray imaging data of knee joints from two different hospitals using Kellgren-Lawrence (K-L) grade of knee OA to help doctors choose proper auxiliary tools. Furthermore, we present attention maps of CNN to highlight the radiological features affecting the network decision. Such information makes the decision process transparent for practitioners, which builds better trust towards such automatic methods and, moreover, reduces the workload of clinicians, especially for remote areas without enough medical staff.

scholarly journals Identification of the most important features of knee osteoarthritis structural progressors using machine learning methods

2020 ◽  
Vol 12 ◽  
pp. 1759720X2093346 ◽  
Author(s):  
Afshin Jamshidi ◽  
Mickael Leclercq ◽  
Aurelie Labbe ◽  
Jean-Pierre Pelletier ◽  
François Abram ◽  
...  
Keyword(s):  
Machine Learning ◽  
Knee Osteoarthritis ◽  
Joint Space ◽  
Partial Least Square ◽  
Classification Methods ◽  
Learning Methods ◽  
Common Features ◽  
Knee Oa ◽  
Machine Learning Methods

Objectives: The aim was to identify the most important features of structural knee osteoarthritis (OA) progressors and classification using machine learning methods. Methods: Participants, features and outcomes were from the Osteoarthritis Initiative. Features were from baseline (1107), including articular knee tissues (135) assessed by quantitative magnetic resonance imaging (MRI). OA progressors were ascertained by four outcomes: cartilage volume loss in medial plateau at 48 and 96 months (Prop_CV_48M, 96M), Kellgren–Lawrence (KL) grade ⩾ 2 and medial joint space narrowing (JSN) ⩾ 1 at 48 months. Six feature selection models were used to identify the common features in each outcome. Six classification methods were applied to measure the accuracy of the selected features in classifying the subjects into progressors and non-progressors. Classification of the best features was done using an automatic machine learning interface and the area under the curve (AUC). To prioritize the top five features, sparse partial least square (sPLS) method was used. Results: For the classification of the best common features in each outcome, Multi-Layer Perceptron (MLP) achieved the highest AUC in Prop_CV_96M, KL and JSN (0.80, 0.88, 0.95), and Gradient Boosting Machine for Prop_CV_48M (0.70). sPLS showed the baseline top five features to predict knee OA progressors are the joint space width, mean cartilage thickness of the medial tibial plateau and sub-regions and JSN. Conclusion: In this comprehensive study using a large number of features ( n = 1107) and MRI outcomes in addition to radiological outcomes, we identified the best features and classification methods for knee OA structural progressors. Data revealed baseline X-ray and MRI-based features could predict early OA knee progressors and that MLP is the best classification method.

scholarly journals Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

2020 ◽  
Vol 493 (3) ◽  
pp. 4209-4228 ◽  
Author(s):  
Ting-Yun Cheng ◽  
Christopher J Conselice ◽  
Alfonso Aragón-Salamanca ◽  
Nan Li ◽  
Asa F L Bluck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Imaging Data ◽  
Morphological Classification ◽  
Learning Methods ◽  
Visual Classification ◽  
Machine Learning Methods ◽  
Dark Energy Survey ◽  
Galaxy Classification ◽  
Energy Survey

ABSTRACT There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ∼2800 galaxies with visual classification from GZ1, we reach an accuracy of ∼0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ∼2.5 per cent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

scholarly journals Diagnose and Prediction of Different Machine Learning methods for Knee Osteoarthritis: a protocol for systematic review and meta-analysis

2021 ◽  
Author(s):  
yu zhou ◽  
Tong Mu ◽  
Xiaochuan Kong ◽  
Le Zhang
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Knee Osteoarthritis ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Joint Disease ◽  
Pool Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Sensitivity Specificity

Abstract Background: Knee osteoarthritis (OA) is a chronic and progressive joint disease with a higher contributor to global disability, mainly in the elderly and particularly in women. The available diagnostic approaches such as X-ray, computed tomography and magnetic resonance imaging have large precision errors and low sensitivity. Machine learning (ML) is the application of probabilistic algorithms to train a computational model to make predictions, it has great potential to become a valuable clinical diagnostic tool. This review aims to determine the diagnosis and prediction accuracy of different machine learning methods for Knee Osteoarthritis Methods: Two reviewers systematically searched Cochrane, PubMed, EMBASE, and Web of Science (last updated in June 2020) for eligible articles. To identify potentially missed publications, the reference lists of the final included studies were manually screened. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (ROC). We will use the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess the risk of bias and applicability. Two independent reviewers will conduct all procedures of study selection, data extraction, and methodological assessment. Any disagreements will be consulted with a third reviewer. RevMan 5.3 software and Stata V15.0 will be used to pool data and to carry out the meta-analysis if it is possible. Results: This systematic review will provide a high-quality synthesis of machine learning for diagnose of knee Osteoarthritis from various evaluation aspects including accuracy, sensitivity, specificity and AUC.Conclusion: The findings of this systematic review will provide latest evidence of diagnosis and prediction of different machine learning for patients with knee Osteoarthritis.Ethics and dissemination: No individual patient data will be used in this study; thus, no ethics approval is needed.Systematic review registration: PROSPERO CRD: 42019133305

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