A Two-Vector Framework for MRI Knee Diagnostics: Fuzzy Risk Modeling, Digital Maturity, and Finite-Element Wear Assessment
Tankibayeva A. Kumargazhanova S. Azamatov B. Azamatova Z. Beisekenov N. Sadenova M.
February 2026Multidisciplinary Digital Publishing Institute (MDPI)
Applied Sciences (Switzerland)
2026#16Issue 3
Featured Application: The framework supports clinical decision-making in knee MRI by jointly quantifying diagnostic risk and departmental digital readiness. A fuzzy-logic module estimates producer’s and consumer’s error probabilities under measurement/interpretation uncertainty, while a digital-maturity index aggregates six weighted capability agents. In parallel, a finite-element (FE) module evaluates TKA contact mechanics, linking imaging reliability to wear-relevant metrics (peak contact pressure 85.449 MPa; UHMWPE von Mises stress 43.686 MPa under (Formula presented.) N at 0° flexion). Together, the tools help target QA actions, optimize protocols, and guide staffing/IT and implant-configuration decisions. 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 (Formula presented.) 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.
diagnostic risk , digital maturity , finite-element analysis , fuzzy logic , knee , MRI , musculoskeletal imaging , quality assurance , simulation modeling , UHMWPE wear
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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, 77843, TX, United States
D. Serikbayev East
Department of Ecology and Conservation Biology
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