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Record W2964905843 · doi:10.1371/journal.pone.0220332

Various 3D printed materials mimic bone ultrasonographically: 3D printed models of the equine cervical articular process joints as a simulator for ultrasound guided intra-articular injections

2019· article· en· W2964905843 on OpenAlexaff
Alexandra Beaulieu, Alex zur Linden, John Phillips, Luis G. Arroyo, Judith Koenig, Gabrielle Monteith

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsSheridan CollegeUniversity of Guelph
Fundersnot available
KeywordsFused deposition modelingMedicine3D printing3d printedStereolithographyBiomedical engineeringSurgeryMaterials science

Abstract

fetched live from OpenAlex

INTRODUCTION: In the equine racehorse industry, reduced athletic performance due to joint injury and lameness has been extensively reviewed. Intra-articular injections of glucocorticoids are routinely used to relieve pain and inflammation associated with osteoarthritis. Intra-articular injections of pharmaceutical agents require practice for precise needle placement and to minimize complications. Training on simulators or models is a viable alternative for developing these technical skills. The purpose of this study was to compare the qualitative ultrasonographic characteristics of three-dimensional (3D) printed models of equine cervical articular process joints to that of a dissected equine cervical spine (gold standard). METHODS: A randomized complete block design study was conducted in which a total of thirteen cervical articular process joint models were printed using several materials, printers, and printing technologies. Ultrasound video clips with the models immersed in water were recorded. Two board certified veterinary radiologists and three veterinary radiology residents reviewed the videos and responded to a survey assessing and comparing the ultrasonographic characteristics of the 3D printed models to those of the gold standard. RESULTS: Six 3D printed models had ultrasonographic characteristics similar to the gold standard. These six models were (material, printer, printing technology): nylon PA 12, EOS Formiga P100, selective laser sintering (P = 0.99); Onyx nylon with chopped carbon fiber, Markforged Onyx Two, fused deposition modeling (P = 0.48); polycarbonate, Ultimaker 3, fused deposition modeling (P = 0.28); gypsum, ProJet CJP 660 Pro, ColorJet Printing (P = 0.28); polylactic acid, Prusa I3, fused deposition modeling (P = 0.23); and high temperature V1 resin, Form 2, stereolithography (P = 0.22). CONCLUSION: When assessed in water, it is possible to replicate the qualitative ultrasonographic characteristics of bone using three dimensional printed models made by combining different materials, printing technologies, and printers. However, not all models share similar qualitative ultrasonographic characteristics with bone. We suggest that the aforementioned six models be used as proxy for simulating bones or joints for use with ultrasound. In order to replicate the resistance and acoustic window provided by soft tissues, further work testing the ability of these models to withstand embedding in material such as ballistic gelatin is required.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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