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Record W2086943821 · doi:10.1118/1.4752205

Measurement of joint kinematics using a conventional clinical single‐perspective flat‐panel radiography system

2012· article· en· W2086943821 on OpenAlexafffund
Petar Seslija, Matthew G. Teeter, Xunhua Yuan, Douglas D.R. Naudie, Robert B. Bourne, Steven J. MacDonald, Terry M. Peters, David W. Holdsworth

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsRobarts Clinical TrialsLondon Health Sciences CentreWestern University
FundersCanadian Institutes of Health ResearchLawson Health Research Institute
KeywordsKinematicsImaging phantomRadiographyComputer visionComputer scienceArtificial intelligenceFluoroscopyAccuracy and precisionBiplaneProjection (relational algebra)MathematicsMedicineAlgorithmNuclear medicineRadiologyPhysicsStatisticsEngineering

Abstract

fetched live from OpenAlex

PURPOSE: The ability to accurately measure joint kinematics is an important tool in studying both normal joint function and pathologies associated with injury and disease. The purpose of this study is to evaluate the efficacy, accuracy, precision, and clinical safety of measuring 3D joint motion using a conventional flat-panel radiography system prior to its application in an in vivo study. METHODS: An automated, image-based tracking algorithm was implemented to measure the three-dimensional pose of a sparse object from a two-dimensional radiographic projection. The algorithm was tested to determine its efficiency and failure rate, defined as the number of image frames where automated tracking failed, or required user intervention. The accuracy and precision of measuring three-dimensional motion were assessed using a robotic controlled, tibiofemoral knee phantom programmed to mimic a subject with a total knee replacement performing a stair ascent activity. Accuracy was assessed by comparing the measurements of the single-plane radiographic tracking technique to those of an optical tracking system, and quantified by the measurement discrepancy between the two systems using the Bland-Altman technique. Precision was assessed through a series of repeated measurements of the tibiofemoral kinematics, and was quantified using the across-trial deviations of the repeated kinematic measurements. The safety of the imaging procedure was assessed by measuring the effective dose of ionizing radiation associated with the x-ray exposures, and analyzing its relative risk to a human subject. RESULTS: The automated tracking algorithm displayed a failure rate of 2% and achieved an average computational throughput of 8 image frames/s. Mean differences between the radiographic and optical measurements for translations and rotations were less than 0.08 mm and 0.07° in-plane, and 0.24 mm and 0.6° out-of-plane. The repeatability of kinematics measurements performed using the radiographic tracking technique was better than ±0.09 mm and 0.12° in-plane, and ±0.70 mm and ±0.07° out-of-plane. The effective dose associated with the imaging protocol used was 15 μSv for 10 s of radiographic cine acquisition. CONCLUSIONS: This study demonstrates the ability to accurately measure knee-joint kinematics using a single-plane radiographic measurement technique. The measurement technique can be easily implemented at most clinical centers equipped with a modern-day radiographic x-ray system. The dose of ionizing radiation associated with the image acquisition represents a minimal risk to any subjects undergoing the examination.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.141
GPT teacher head0.340
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2012
Admission routes2
Has abstractyes

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