Combining Knee Acoustic Emissions, Patient-Reported Measures, and Machine Learning to Assess Osteoarthritis Severity-Reference-Cited by-同舟云学术

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Combining Knee Acoustic Emissions, Patient-Reported Measures, and Machine Learning to Assess Osteoarthritis Severity

Published:2023-10-09 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 19th International Conference on Body Sensor Networks (BSN)
language:
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Author:

Nichols Christopher¹,Crane H. Trask²,Ewart Dave³,Inan Omer T.⁴

Affiliation:

1. Georgia Institute of Technology,Bioengineering Graduate Program,Atlanta,GA,USA

2. Georgia Institute of Technology,School of Electrical and Computer Engineering,Atlanta,GA,USA

3. Minneapolis Veterans Affairs Medical Center,Minneapolis,MN,USA

4. Georgia Institute of Technology,School of Electrical and Computer Engineering,Coulter Dept. of Biomedical Engineering,Atlanta,GA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10330797/10330909/10331381.pdf?arnumber=10331381

Reference11 articles.

1. Number of Persons With Symptomatic Knee Osteoarthritis in the US: Impact of Race and Ethnicity, Age, Sex, and Obesity

2. Diagnosis of osteoarthritis: Imaging

3. The relationship between cartilage loss on magnetic resonance imaging and radiographic progression in men and women with knee osteoarthritis

4. Radiological Assessment of Osteo-Arthrosis

5. Disease-modifying treatments for osteoarthritis (DMOADs) of the knee and hip: lessons learned from failures and opportunities for the future

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review;IEEE Access;2024

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