Knee Osteoarthritis Diagnosis: Future and Perspectives
The risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to...
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MDPI AG
2025-07-01
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| author | Henri Favreau Kirsley Chennen Sylvain Feruglio Elise Perennes Nicolas Anton Thierry Vandamme Nadia Jessel Olivier Poch Guillaume Conzatti |
| author_facet | Henri Favreau Kirsley Chennen Sylvain Feruglio Elise Perennes Nicolas Anton Thierry Vandamme Nadia Jessel Olivier Poch Guillaume Conzatti |
| author_sort | Henri Favreau |
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| description | The risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to effectively monitor, evaluate, and predict the evolution of KOA before and during the therapeutic protocol. In this review, we summarize the main methods that are used or seem promising for the diagnosis of osteoarthritis, with a specific focus on non- or low-invasive methods. As standard diagnostic tools, arthroscopy, magnetic resonance imaging (MRI), and X-ray radiography provide spatial and direct visualization of the joint. However, discrepancies between findings and patient feelings often occur, indicating a lack of correlation between current imaging methods and clinical symptoms. Alternative strategies are in development, including the analysis of biochemical markers or acoustic emission recordings. These methods have undergone deep development and propose, with non- or minimally invasive procedures, to obtain data on tissue condition. However, they present some drawbacks, such as possible interference or the lack of direct visualization of the tissue. Other original methods show strong potential in the field of KOA monitoring, such as electrical bioimpedance or near-infrared spectrometry. These methods could permit us to obtain cheap, portable, and non-invasive data on joint tissue health, while they still need strong implementation to be validated. Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. This would offer a powerful tool for researchers and clinicians to improve therapeutic strategies while permitting an anticipated adaptation of the clinical protocols, moving toward reliable and personalized medicine. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Biomedicines |
| spelling | doaj-art-9d8e4097c2bb4a2392aec7adcfc81d782025-08-20T03:36:35ZengMDPI AGBiomedicines2227-90592025-07-01137164410.3390/biomedicines13071644Knee Osteoarthritis Diagnosis: Future and PerspectivesHenri Favreau0Kirsley Chennen1Sylvain Feruglio2Elise Perennes3Nicolas Anton4Thierry Vandamme5Nadia Jessel6Olivier Poch7Guillaume Conzatti8Université de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, FranceLaboratory of Medical Genetics, INSERM U1112, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, FranceSorbonne Université, Laboratoire d’Informatique de Paris 6 (LIP6), CNRS UMR7606, 4 Place Jussieu, CEDEX 05, 75252 Paris, FranceUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, FranceUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, FranceUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, FranceUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, FranceUniversité de Strasbourg, Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, CNRS UMR 7357, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, FranceUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, FranceThe risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to effectively monitor, evaluate, and predict the evolution of KOA before and during the therapeutic protocol. In this review, we summarize the main methods that are used or seem promising for the diagnosis of osteoarthritis, with a specific focus on non- or low-invasive methods. As standard diagnostic tools, arthroscopy, magnetic resonance imaging (MRI), and X-ray radiography provide spatial and direct visualization of the joint. However, discrepancies between findings and patient feelings often occur, indicating a lack of correlation between current imaging methods and clinical symptoms. Alternative strategies are in development, including the analysis of biochemical markers or acoustic emission recordings. These methods have undergone deep development and propose, with non- or minimally invasive procedures, to obtain data on tissue condition. However, they present some drawbacks, such as possible interference or the lack of direct visualization of the tissue. Other original methods show strong potential in the field of KOA monitoring, such as electrical bioimpedance or near-infrared spectrometry. These methods could permit us to obtain cheap, portable, and non-invasive data on joint tissue health, while they still need strong implementation to be validated. Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. This would offer a powerful tool for researchers and clinicians to improve therapeutic strategies while permitting an anticipated adaptation of the clinical protocols, moving toward reliable and personalized medicine.https://www.mdpi.com/2227-9059/13/7/1644diagnosismedical deviceosteoarthritisartificial intelligence |
| spellingShingle | Henri Favreau Kirsley Chennen Sylvain Feruglio Elise Perennes Nicolas Anton Thierry Vandamme Nadia Jessel Olivier Poch Guillaume Conzatti Knee Osteoarthritis Diagnosis: Future and Perspectives Biomedicines diagnosis medical device osteoarthritis artificial intelligence |
| title | Knee Osteoarthritis Diagnosis: Future and Perspectives |
| title_full | Knee Osteoarthritis Diagnosis: Future and Perspectives |
| title_fullStr | Knee Osteoarthritis Diagnosis: Future and Perspectives |
| title_full_unstemmed | Knee Osteoarthritis Diagnosis: Future and Perspectives |
| title_short | Knee Osteoarthritis Diagnosis: Future and Perspectives |
| title_sort | knee osteoarthritis diagnosis future and perspectives |
| topic | diagnosis medical device osteoarthritis artificial intelligence |
| url | https://www.mdpi.com/2227-9059/13/7/1644 |
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