Evidence map›Paper›PMID 39856682›Full record

ArticleBMC medical informatics and decision making2025

Feasibility of YOLOX computer model-based assessment of knee function compared with manual assessment for people with severe knee osteoarthritis.

Tao Yang, Jie Zhao, Ben Wang, Li Wang, Hengzhe Bao, Bing Li, Wen Luo, Huiwen Zhao, Jun Liu

Abstract readComparative Study
In one paragraph

Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Tao Yang *Joint Surgery Department, Tianjin Hospital, No. 406, Jiefangnan Road, Tianjin, 300211, People's Republic of China.
Jie Zhao *Orthopedics Department, Tianjin Hospital, Tianjin, People's Republic of China.
Ben WangCollege of Orthopedics, Tianjin Medical University, Tianjin, People's Republic of China.
Li WangCollege of Orthopedics, Tianjin Medical University, Tianjin, People's Republic of China.
Hengzhe BaoCollege of Orthopedics, Tianjin Medical University, Tianjin, People's Republic of China.
Bing LiJoint Surgery Department, Tianjin Hospital, No. 406, Jiefangnan Road, Tianjin, 300211, People's Republic of China.
Wen LuoJoint Surgery Department, Tianjin Hospital, No. 406, Jiefangnan Road, Tianjin, 300211, People's Republic of China.
Huiwen ZhaoJoint Surgery Department, Tianjin Hospital, No. 406, Jiefangnan Road, Tianjin, 300211, People's Republic of China.
Jun LiuJoint Surgery Department, Tianjin Hospital, No. 406, Jiefangnan Road, Tianjin, 300211, People's Republic of China. drliujun1968@126.com.

Funding

National Natural Science Foundation of China 82102639Tianjin Natural Science Foundation 22JCQNJC00850Tianjin Science and Technology Commission (TSTC) Diversified Fund 21JCZDJC01000
6 · The paper itself

Abstract

objectiveThis study aimed to assess the feasibility of computer model-based evaluation of knee joint functional capacity in comparison with manual assessment.

methodsThis study consisted of two phases: (1) developing an automatic knee joint action recognition and classification system on the basis of improved YOLOX and (2) analyzing the feasibility of assessment by the software system and doctors, identifying the knee joint function of patients, and determining the accuracy of the software system. We collected 40-50 samples for use in clinical experiments. The datasets used in this study were collected from patients admitted to the Joint Surgery Center. In this study, the knee joint assessment items included stair climbing, walking on uneven surfaces, and knee joint function. To assess the computer model's automatic evaluation of knee joint function, MedCalc 20 statistical software was used to analyze the consistency of the Lequesne functional index between the computer model's automated determinations and manual independent assessments.

resultsThe weighted kappa coefficients between the doctors' assessments and the software system's assessments were 0.76 (95% confidence intervals:0.59 ~ 0.92) for climbing up and down stairs, 0.64 (95% confidence intervals:0.45 ~ 0.82) for walking on uneven floors, and 0.68 (95% confidence intervals:0.53 ~ 0.84) for the Lequesne functional index, indicating good consistency between the assessments of the software system and doctors.

conclusionThis paper introduces an automatic knee joint action recognition and classification method based on improved YOLOX. By comparing the results obtained by orthopedic doctors and the software system, the feasibility of this software system was validated in the clinic.

Indexed as

AlgorithmsComputer SimulationKneeOsteoarthritis, KneeAgedBayes TheoremCalibrationFeasibility StudiesFemaleHumansMaleStair ClimbingWalkingAssessmentDeep learningFunctionKnee

Identifiers

PMID39856682
PMCPMC11763141

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