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Record W7045592700

Biomechanical analysis of the pelvic floor and pessary device design

2023· dissertation· en· W7045592700 on OpenAlexfundno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsnot available
FundersUniversity of WaterlooMitacs
KeywordsPelvic floorPessaryBiomechanicsPalpationUltrasound3D ultrasoundPelvic Floor Muscle
DOInot available

Abstract

fetched live from OpenAlex

Pelvic floor disorders (PFD) including pelvic organ prolapse (POP) can significantly impact women’s quality of life. Pessaries, removable gynecological prosthetic devices, provide mechanical support for temporary or long-term symptom relief of PFDs like POP. To investigate both PFDs and pessaries this thesis focused on two themes: (1) image-based analysis of dynamic pelvic floor contractions and (2) mechanical characterization of pessaries using a combination of experimental and computational methods. \nPFDs are commonly assessed by pelvic floor muscle strength during vaginal contraction, often performed through subjective digital palpation or ultrasound imaging. However, vaginal wall movement can be difficult to interpret. An alternative is colpodynamic imaging (CDI), a novel approach that uses dynamic trans-introital ultrasound combined with intra-vaginal pressure and volume monitoring of vaginal distension achieved with an inflated thin bag, to improve visualization of the vaginal wall during contraction. \nThe goal of theme (1) was to characterize vaginal contraction biomechanics using a CDI approach. As a first step, 3D Slicer was used to segment the bag shape from 2D CDI ultrasound videos captured during contraction from 19 patients with diagnosed prolapse. The bag shape was defined at common 1cm inferior-superior intervals. Several CDI measurements were extracted during voluntary contraction and averaged using ARCGen in MATLAB. The decrease in average patient bag dimensions exhibited consistent trapezoidal trends, an inverse of the pressure changes observed. This represents the first-time dynamic distended pelvic floor contraction has been evaluated, demonstrating good visibility of the vaginal wall and trends in the contraction pressure and shape change. Next steps include increasing the CDI dataset and evaluating patients without prolapse. \nWith respect to theme (2), mechanical tests are commonly performed on physical pessary designs to characterize their behaviour under load; however, pessary manufacturing is expensive and time consuming. As an alternative, finite element (FE) modelling can provide detailed numerical insight into the response of a pessary under load but to date has seen limited application, with little data available for pessary silicone materials. The goal of the first study of Theme 1 aimed to identify hyperelastic material models for two silicone materials used in pessary manufacturing towards FE analysis of ring with support (RWS) pessaries. It was hypothesized that hyperelastic material models could be identified to sufficiently capture the force and deformation response of multiple RWS sizes under different boundary conditions and silicone material types (Shore 60A and 40A). To understand the material characteristics of pessary silicone, uniaxial tension and compression tests were performed using “dog-bone” and cylinder test samples. The uniaxial experimental data was fit with Mooney-Rivlin (MR) material models. To ensure the material models characterize the pessary behaviour, data from mechanical tests representing RWS pessary folding and modified 3-point bending were compared to FE recreations of the same tests with the MR materials applied. The FE model results demonstrated excellent agreement in the force-displacement response for both tests with different pessary sizes and silicones. \nA second study within Theme 2 aimed to investigate the use of topology optimization to perform a potential redesign of a generic Gellhorn pessary. The goal of this iterative redesign was to improve pessary insertion and removal while optimizing based on expected boundary conditions within the vaginal canal. To achieve the redesign goals, the Gellhorn cap and knob were recreated in Altair Inspire with an open design space between them, allowing maximum stiffness and minimize mass optimization algorithms to create two optimal stem designs. The parameters for these optimizations included boundary conditions to represent folding for insertion and increased pressures due to a Valsalva maneuver. The results of these optimizations were utilized to redesign the Gellhorn while considering features to improve gripping for insertion and removal. These redesigned features were then analyzed in Altair Inspire to ensure they met the needs of the problem space. This resulted in a tripod stem for directional folding, handles on the stem and cap for gripping as well as a cut-out for releasing pessary suction. \nCollectively, the three studies represent novel investigations of pelvic floor contraction and pessary device design. All three studies could result in improved pessary treatment by better understanding pelvic floor biomechanics and improving pessary design. Future work on the CDI technique could expand on the developed approach to better understand different pelvic floor impairments as compared to healthy anatomy. Further, the use of topology optimization design methods could be extended to other pessary types as well as adopting different patient dimensions and potential loading conditions.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.240
Teacher spread0.219 · 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
GenreMethods

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

Citations0
Published2023
Admission routes1
Has abstractyes

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