A Two-Vector Framework for MRI Knee Diagnostics: Fuzzy Risk Modeling, Digital Maturity, and Finite-Element Wear Assessment
Source abstract
Abstract
Knee disorders are a major indication for musculoskeletal imaging, yet MRI reliability remains constrained by signal nonuniformity, motion artefacts, protocol variability, and reader-dependent effects. This study presents an integrated two-vector framework that couples (i) a fuzzy diagnostic control-risk model with (ii) a quantitative digital-maturity assessment to strengthen MRI-based diagnosis of knee pathology. The vertical vector characterizes organizational readiness through a weighted fuzzy aggregation of six capability agents (technical, information and analytical, mathematical/model, metrological, human resources, and software support). The horizontal vector estimates producer’s and consumer’s risks as misclassification probabilities relative to an acceptance boundary, driven by measurement/interpretation uncertainty, variability of the decision threshold, and the ratio of instrumental to physiological dispersion. Simulation results indicate that error probabilities increase sharply when threshold uncertainty exceeds 20–25% and rise by approximately 15–20% as the standard-deviation ratio approaches unity. To connect diagnostic reliability with downstream mechanics, a FE analysis of the tibial insert in TKA under F=1150 N at 0° flexion predicts a peak contact pressure of 85.449 MPa and a maximum UHMWPE von Mises stress of 43.686 MPa, identifying wear-critical contact zones. Overall, the proposed framework provides interpretable quantitative targets for QA, protocol refinement, and resource allocation in radiology services undergoing digital transformation, and offers a reproducible pathway for linking imaging reliability to biomechanical risk.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Total Knee Arthroplasty Outcomes · Osteoarthritis Treatment and Mechanisms · Knee injuries and reconstruction techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
D. Serikbayev East Kazakhstan Technical University, 19 Serikbayev Str., Ust-Kamenogorsk 070000, Kazakhstan
Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX 77843, USA
Funding
The Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan
References (39)
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