Article · Applied Sciences · February 3, 2026
A Two-Vector Framework for MRI Knee Diagnostics: Fuzzy Risk Modeling, Digital Maturity, and Finite-Element Wear Assessment
Akerke Tankibayeva, Saule K. Kumargazhanova, B. N. Azamatov, Zhanerke Azamatova, Nail Beisekenov, Marzhan Anuarbekovna Sadenova
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.
More by Nail Beisekenov
- Sustainable lake-resort tourism under water-quality constraints: an integrated monitoring-carrying-capacity framework for destination managementTourism Recreation Research · 2026
- Delft3D Model of Nature-Based Solutions: Comparing a Sand Engine, Living Shoreline, and Reef at Sand Point and Keller Bay, TexasJournal of Waterway Port Coastal and Ocean Engineering · 2026
- Data for "Detecting tipping thresholds in U.S. tidal wetlands using machine learning"Zenodo · 2026
- Recovery of Secondary Metals and Concrete Modification from Recycled PC Electronic WasteRecycling · 2026 · Citations: 1
