An affordable and easy-to-use tool to diagnose knee arthritis using knee sound

Emadi Andani, Mehran; Salehi, Zahra

doi:10.1016/j.bspc.2023.105685

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…High resolution medical imaging, particularly magnetic resonance imaging (MRI), offers a powerful tool for such methods since it provides information regarding the state of a knee joint before the disease has progressed significantly and symptoms are detectable externally, such as with physical tests and patient-reported pain assessment scales. Other non-invasive diagnostic methods such as X-ray [ 5 ] and vibroarthrography [ 6 , 7 , 8 ], have been explored as well, though X-ray has low sensitivity when detecting KOA in the critical early stages [ 9 ] and vibroarthography is still being explored [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models

Yu,

Yang,

Lartey

et al. 2024

Bioengineering

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Bone marrow edema-like lesions (BMEL) in the knee have been linked to the symptoms and progression of osteoarthritis (OA), a highly prevalent disease with profound public health implications. Manual and semi-automatic segmentations of BMELs in magnetic resonance images (MRI) have been used to quantify the significance of BMELs. However, their utilization is hampered by the labor-intensive and time-consuming nature of the process as well as by annotator bias, especially since BMELs exhibit various sizes and irregular shapes with diffuse signal that lead to poor intra- and inter-rater reliability. In this study, we propose a novel unsupervised method for fully automated segmentation of BMELs that leverages conditional diffusion models, multiple MRI sequences that have different contrast of BMELs, and anomaly detection that do not rely on costly and error-prone annotations. We also analyze BMEL segmentation annotations from multiple experts, reporting intra-/inter-rater variability and setting better benchmarks for BMEL segmentation performance.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models

Yu,

Yang,

Lartey

et al. 2024

Bioengineering

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“…Over the course of last century, VAG showed in preclinical evaluation to be effective in determining cartilage loss with accuracy surpassing 90 percent (Andersen et al, 2016). Both acoustic and vibration emission have been proposed and evaluated in the research (Emadi Andani & Salehi, 2024;Łysiak et al, 2020). However, up to this date there is no specific examination nor acquisition protocols that are widely accepted by the research society.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes

KARPIŃSKI,

KRAKOWSKI,

JONAK

et al. 2023

Appl. Comput. Sci.

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Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.

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A semi-automatic cardiovascular annotation and quantification toolbox utilizing prior knowledge-guided feature learning

Zhang,

Cao,

et al. 2025

Biomedical Signal Processing and Control

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An affordable and easy-to-use tool to diagnose knee arthritis using knee sound

Cited by 5 publications

References 25 publications

Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models

Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models

Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes

A semi-automatic cardiovascular annotation and quantification toolbox utilizing prior knowledge-guided feature learning

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