Ultra‐high‐frequency ultrasound: promising technique to visualize pelvic floor mesh <i>in vivo</i>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".