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Advances in application of artificial intelligence in diagnosis and progress prediction of knee osteoarthritis
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Hai-Tao Yu1, 2, Hao-Yue Wu2, Hao-Qiang Zhang2, Chen-Po Dang3, Xu-Sheng Li1, 2, *
Medical Journal of Chinese People’s Liberation Army | 2025, 50(1) : 9 - 15
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Medical Journal of Chinese People’s Liberation Army | 2025, 50(1): 9-15
Special Issue on Application of Artificial Intelligence in Disease Diagnosis and Treatment
Advances in application of artificial intelligence in diagnosis and progress prediction of knee osteoarthritis
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Hai-Tao Yu1, 2, Hao-Yue Wu2, Hao-Qiang Zhang2, Chen-Po Dang3, Xu-Sheng Li1, 2, *
Affiliations
  • 1The First School of Clinical Medicine of Gansu University of Chinese Medicine, Lanzhou, Gansu 730030, China
  • 2Department of Joint Surgery, the 940th Hospital of Joint Logistic Support Force of Chinese PLA, Lanzhou, Gansu 730050, China
  • 3Department of Sports Medicine, the 940th Hospital of Joint Logistic Support Force of Chinese PLA, Lanzhou, Gansu 730050, China
Published: 2025-01-28 doi: 10.11855/j.issn.0577-7402.0023.2024.0307
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Knee osteoarthritis (KOA) is a chronic degenerative joint disease, which poses a major challenge particularly among the elderly population due to its high incidence and high disability. Imaging examination has been used commonly to diagnose KOA. However, it faces imitations in predicting disease progression due to the lack of prior information and constraints in manpower and time. With the rapid evolution of big data and computational technologies, artificial intelligence (AI) is progressively integrating into various healthcare domains. Therefore, the integration of artificial intelligence (AI) into healthcare holds promise for revolutionizing KOA diagnosis and treatment. AI-assisted diagnostic models have demonstrated the potential to automate diagnosis, classify disease severity, and predict disease progression with improved efficiency and accuracy. In addition, these models provide personalized diagnosis and treatment options, as well as accurate disease progression risk assessment. Despite these promising outcomes, challenges such as high costs associated with data annotation and limitations in model generalization capabilities persist. This paper reviews recent advancements in AI applications and summarizes the potential value of utilizing AI applications for KOA. To further enhance the utilization of AI in KOA management to overcome current limitations, future efforts should focus on standardizing clinical sample databases, optimizing AI algorithms, and enhancing external verification sets.

osteoarthritis  /  artificial intelligence  /  machine learning  /  deep learning
Hai-Tao Yu, Hao-Yue Wu, Hao-Qiang Zhang, Chen-Po Dang, Xu-Sheng Li. Advances in application of artificial intelligence in diagnosis and progress prediction of knee osteoarthritis[J]. Medical Journal of Chinese People’s Liberation Army, 2025 , 50 (1) : 9 -15 . DOI: 10.11855/j.issn.0577-7402.0023.2024.0307
  • Science and Technology Plan Project of Gansu Province(20JR10RA008)
  • Scientific Research Project of Health Industry in Gansu Province(GSWSKY-2019-12)
  • Special Topic of Prevention and Treatment of Training Injuries of PLA(21XLS24)
  • Innovation Project of Young Scientific and Technological Talents in Lanzhou(2023-2-28)
  • Innovation Project of Young Scientific and Technological Talents in Lanzhou(2019-RC-65)
  • Research Project in the 940th Hospital of Joint Logistic Support Force of Chinese PLA(2023YXKY014)
  • Research Project in the 940th Hospital of Joint Logistic Support Force of Chinese PLA(2021YXKY009)
Year 2025 volume 50 Issue 1
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213
96
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Article Info
doi: 10.11855/j.issn.0577-7402.0023.2024.0307
  • Receive Date:2024-01-07
  • Online Date:2025-11-10
  • Published:2025-01-28
Article Data
Affiliations
History
  • Received:2024-01-07
  • Accepted:2024-01-22
Funding
Science and Technology Plan Project of Gansu Province(20JR10RA008)
Scientific Research Project of Health Industry in Gansu Province(GSWSKY-2019-12)
Special Topic of Prevention and Treatment of Training Injuries of PLA(21XLS24)
Innovation Project of Young Scientific and Technological Talents in Lanzhou(2023-2-28)
Innovation Project of Young Scientific and Technological Talents in Lanzhou(2019-RC-65)
Research Project in the 940th Hospital of Joint Logistic Support Force of Chinese PLA(2023YXKY014)
Research Project in the 940th Hospital of Joint Logistic Support Force of Chinese PLA(2021YXKY009)
Affiliations
    1The First School of Clinical Medicine of Gansu University of Chinese Medicine, Lanzhou, Gansu 730030, China
    2Department of Joint Surgery, the 940th Hospital of Joint Logistic Support Force of Chinese PLA, Lanzhou, Gansu 730050, China
    3Department of Sports Medicine, the 940th Hospital of Joint Logistic Support Force of Chinese PLA, Lanzhou, Gansu 730050, China

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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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