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Record W4409795916 · doi:10.1109/tmi.2025.3564521

Monitoring Knee Health: Ultra-Wideband Radar Imaging for Early Detection of Osteoarthritis

2025· article· en· W4409795916 on OpenAlexaff
Kapil Gangwar, Robert Winter, Fatemeh Modares Sabzevari, Gary Chien-Yi Chen, Kevin Chan, Karumudi Rambabu

Bibliographic record

VenueIEEE Transactions on Medical Imaging · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOsteoarthritisKnee JointFemurTibiaComputer scienceBiomedical engineeringRadarMedicineArtificial intelligenceAnatomyTelecommunicationsSurgeryPathology

Abstract

fetched live from OpenAlex

This paper presents a non-invasive method and study for analyzing knee osteoarthritis, encompassing a dual-step approach: a) the employment of synthetic aperture radar (SAR)-based microwave reflection tomography for imaging the knee joint, and b) the application of an ultra-wideband (UWB) radar technique combined with a genetic algorithm to determine muscle electrical properties (permittivity) and the gap between the femur (thighbone) and tibia (shinbone). The assessment of osteoarthritis is conducted by integrating the outcomes of the knee joint imaging, change in muscle permittivity, and inter-bone spacing. This technique undergoes initial validation on simplified knee models, subsequently extending to adult human voxel knee tissues as represented in CST software. Experimental validation involves analyzing a porcine knee joint comprising sequential layers of skin, fat, muscle, and bone. Both simulated and experimental validations suggest that this technique is viable, safe, and cost-effective for estimating knee osteoarthritis in humans.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.246
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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