A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative

Brahim, Abdelbasset; Jennane, Rachid; Riad, Rabia; Janvier, Thomas; Khedher, L.; Toumi, Hechmi; Lespessailles, Éric

doi:10.1016/j.compmedimag.2019.01.007

Cited by 139 publications

(63 citation statements)

References 29 publications

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“…Hirvasniemi applied machine learning to predict the incidence of radiographic hip OA using 986 images. Brahim et al [30] applied machine learning (multivariate linear regression) to detect early knee OA using knee X-ray. Most of the previous studies focused on image data with machine learning or deep learning with image data, such as MRI or X-ray.…”

Section: Related Workmentioning

confidence: 99%

A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data

Lim

Kim

Cheon

2019

IJERPH

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A large number of people suffer from certain types of osteoarthritis, such as knee, hip, and spine osteoarthritis. A correct prediction of osteoarthritis is an essential step to effectively diagnose and prevent severe osteoarthritis. Osteoarthritis is commonly diagnosed by experts through manual inspection of patients’ medical images, which are usually collected in hospitals. Checking the occurrence of osteoarthritis is somewhat time-consuming for patients. In addition, the current studies are focused on automatically detecting osteoarthritis through image-based deep learning algorithms. This needs patients’ medical images, which requires patients to visit the hospital. However, medical utilization and health behavior information as statistical data are easier to collect and access than medical images. Using indirect statistical data without any medical images to predict the occurrence of diverse forms of OA can have significant impacts on pro-active and preventive medical care. In this study, we used a deep neural network for detecting the occurrence of osteoarthritis using patient’s statistical data of medical utilization and health behavior information. The study was based on 5749 subjects. Principal component analysis with quantile transformer scaling was employed to generate features from the patients’ simple background medical records and identify the occurrence of osteoarthritis. Our experiments showed that the proposed method using deep neural network with scaled PCA resulted in 76.8% of area under the curve (AUC) and minimized the effort to generate features. Hence, this methos can be a promising tool for patients and doctors to prescreen for possible osteoarthritis to reduce health costs and patients’ time in hospitals.

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Section: Related Workmentioning

confidence: 99%

A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data

Lim

Kim

Cheon

2019

IJERPH

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“…The standardization utilizing MLR permitted us to not only lessen the inter-subject changeability but also expand the partition between CC and OA gatherings. Further investigation uncovered that the proposed framework can provide high-classification execution in recognizing solid and osteoarthritic patients from various knee sides [41].…”

Section: S27mentioning

confidence: 98%

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

et al. 2020

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The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

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“…Moreover, Lim et al used statistical data in a deep neural network with scaled Principal Component Analysis (PCA) for the early detection of KOA [14]. Furthermore, Brahim et al presented a computer aided diagnosis (CAD) system for early KOA detection employing X-ray imaging and ML algorithms [15]. The proposed method achieved an 82.98% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

Ntakolia

Kokkotis

Moustakidis³

et al. 2021

Diagnostics

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Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined. A machine learning approach is proposed with the use of multidisciplinary data from the osteoarthritis initiative database. The proposed methodology employs: (i) A clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection (FS) process consisting of filter, wrapper, and embedded techniques that identifies the most informative risk factors; (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final predictive model for JSN; and (iv) post-hoc interpretation of the features’ impact on the best performing model. The results showed that bounding the JSN progression of both knees can result to more robust prediction models with a higher accuracy (83.3%) and with fewer risk factors (29) compared to the right knee (77.7%, 88 risk factors) and the left knee (78.3%, 164 risk factors), separately.

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A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative

Cited by 139 publications

References 29 publications

A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data

A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

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