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Record W3038745753 · doi:10.1002/uog.22137

Ultra‐high‐frequency ultrasound: promising technique to visualize pelvic floor mesh <i>in vivo</i>

2020· letter· en· W3038745753 on OpenAlexaffabout
Arnoud W. Kastelein, Boris C. de Graaf, Yani P. Latul, Kim W. J. Verhorstert, Joost Holthof, Zeliha Güler, Jan‐Paul Roovers

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2020
Typeletter
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsFujiFilm VisualSonics (Canada)
Fundersnot available
KeywordsBiomedical engineeringMedicineUltrasoundImplantPolygon meshVisualizationIn vivo3D ultrasoundTransonicNuclear medicineRadiologySurgeryGeometryComputer science

Abstract

fetched live from OpenAlex

The in-vivo behavior of pelvic floor implants and the foreign-body response they induce show great interpatient variability, depending on both patient and implant characteristics. The configuration and textural properties of the implant, such as pore size and shape, may change after implantation, which in turn affects the foreign-body response1. A method to study accurately pelvic floor implants in vivo, longitudinally and non-invasively, is currently lacking. A recent study demonstrated that ultra-high-frequency ultrasound (UHFU) offers improved quality of vaginal imaging compared to conventional ultrasound, allowing clear visualization of the vaginal walls2. We investigated whether UHFU also allows for detailed visualization of vaginal meshes. We used an UHFU system (Vevo 3100, Fujifilm VisualSonics Inc., Toronto, ON, Canada) with transducers with respective center frequencies of 40 MHz (MX550: axial resolution, 40 µm) and 20 MHz (MX250: axial resolution, 75 µm) to image two different mesh constructs: a polypropylene (PP) mesh (Restorelle DirectFix Mesh, Coloplast, Minneapolis, MN, USA) and a fully biodegradable poly-4-hydroxybutyrate (P4HB) ‘diamond’ construct mesh (Tepha, Lexington, MA, USA) in two different experimental setups. The mesh constructs were imaged in vitro in transonic gel and in a simulated in-vivo setup in an ex-vivo chicken breast (total thickness, 40 mm). We performed two-dimensional cross-sectional imaging and three-dimensional (3D) brightness (B)-mode imaging, and measured pore size and cross-sectional fiber surface area of the different materials. Imaging using UHFU resulted in clear visualization of both the PP and P4HB vaginal mesh constructs in transonic gel. 3D B-mode allowed for 3D reconstruction of the mesh construct and subtraction of the surroundings (Figure 1a,b). Cross-sectional imaging allowed for accurate measurements of pore size and fiber surface area (Figure 1c). Imaging was also feasible in the simulated in-vivo setup (Figure 2). These results suggest that UHFU is a promising technique for non-invasive imaging of pelvic floor implants, allowing for quantification of shrinkage, pore stability and degradation of (future) pelvic floor implants. Additionally, UHFU add-ons, such as power Doppler and photoacoustic imaging, may facilitate ultrasensitive analysis of parameters of the foreign-body response, such as angiogenesis (important for mesh integration), fibrosis (considered the root of many mesh-related adverse events) and bacterial biofilm formation3-5. UHFU also has the potential to reduce the sample size of animal studies evaluating newly designed pelvic floor implants, since the animals do not have to be euthanized in order to perform ex-vivo analyses of the implanted material at multiple timepoints. Future studies should ascertain whether in-vivo imaging of vaginal mesh can be performed in comparable detail to that in the current in-vitro study. Furthermore, the correlation between in-vivo imaging and histology should be assessed, as well as the additional value of the mentioned imaging add-ons for assessment of angiogenesis, fibrosis and biofilm formation, and their correlation with ex-vivo, histological measurement.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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