Deep Learning-Based Multimodal 3 T MRI for the Diagnosis of Knee Osteoarthritis

Hu, Yong; Tang, Jie; Zhao, Shenghao; Li, Ye

doi:10.1155/2022/7643487

Cited by 5 publications

(4 citation statements)

References 27 publications

(21 reference statements)

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“…The authors in [21] developed and analyzed the effectiveness of the AlexNeT model in predicting and classifying knee OA from X-ray images. In [22], the authors proposed an image super-resolution algorithm based on an improved multiscale wide residual network model for detecting knee OA from knee MRI scans. This algorithm achieved a classification accuracy of up to 95%.…”

Section: Previous Workmentioning

confidence: 99%

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

Alshamrani

Rashid²,

Alshamrani

et al. 2023

Healthcare

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Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%.

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Section: Previous Workmentioning

confidence: 99%

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

Alshamrani

Rashid²,

Alshamrani

et al. 2023

Healthcare

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show abstract

“…In [ 41 ], the cartilage injury assessment of KOA was studied by using an image superresolution algorithm based on an improved multiscale wide residual network model together with several MRI sequences. The performance of the proposed model was compared with the single-shot multibox detector, superresolution CNN, and enhanced deep superresolution algorithms.…”

Section: Applications Of Artificial Intelligence In Knee Osteoarthritismentioning

confidence: 99%

Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022

Özkan

Deniz

2023

Osteoarthritis Imaging

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“…Hu et al . 213 employed a deep-learning model (image super-resolution algorithm based on an improved multiscale comprehensive residual network) combined with an MRI sequence to evaluate the cartilage injury in knee OA as evaluated by arthroscopy (outcome, injury grades I–IV). Compared to the different MRI sequences (T1-weighted, proton density-weighted with fat saturation (PDWI-FS), coronal PDWI-FS, axial T2-weighted, T2, T2*, and T1), the 3D sagittal double-echo stable water excitation was the best MRI sequence with AUCs 0.85, 0.72, 0.85, and 0.97 for grades I, II, III, and IV lesions, respectively.…”

Section: Prediction Of Knee Osteoarthritis Diagnosis and Prognosis Us...mentioning

confidence: 99%

Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis

Martel‐Pelletier

Paiement

Pelletier

2023

Therapeutic Advances in Musculoskeletal

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Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, and this could be achieved by allowing knee tissue visualization and quantifying their changes over time. Although some imaging modalities are available for knee structure assessment, magnetic resonance imaging (MRI) is preferred. This narrative review looks at existing literature, first on MRI-developed approaches for evaluating knee articular tissues, and second on prediction using machine/deep-learning-based methodologies and MRI as input or outcome for early OA diagnosis and prognosis. A substantial number of MRI methodologies have been developed to assess several knee tissues in a semi-quantitative and quantitative fashion using manual, semi-automated and fully automated systems. This dynamic field has grown substantially since the advent of machine/deep learning. Another active area is predictive modelling using machine/deep-learning methodologies enabling robust early OA diagnosis/prognosis. Moreover, incorporating MRI markers as input/outcome in such predictive models is important for a more accurate OA structural diagnosis/prognosis. The main limitation of their usage is the ability to move them in rheumatology practice. In conclusion, MRI knee tissue determination and quantification provide early indicators for individuals at high risk of developing this disease or for patient prognosis. Such assessment of knee tissues, combined with the development of models/tools from machine/deep learning using, in addition to other parameters, MRI markers for early diagnosis/prognosis, will maximize opportunities for individualized risk assessment for use in clinical practice permitting precision medicine. Future efforts should be made to integrate such prediction models into open access, allowing early disease management to prevent or delay the OA outcome.

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Deep Learning-Based Multimodal 3 T MRI for the Diagnosis of Knee Osteoarthritis

Cited by 5 publications

References 27 publications

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022

Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis

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