Finite-element modelling of interactions of needle with tympanic membrane and middle ear
Bibliographic record
Abstract
• Finite-element analysis offers insights into needle-TM interactions for middle-ear interventions. • Combined 2D and 3D FE modeling strategy reveals TM deformation during needle insertion. • First-order Ogden hyperelastic model accurately captures TM mechanical nonlinearity. • TM thickness, needle geometry, and material impact needle insertion force and success. • Methodology aids in developing therapeutic and diagnostic technologies for hearing and balance pathologies. The tympanic membrane (TM) is one of the most common routes to access the middle ear and inner ear for the treatment of hearing and balance pathologies. Since the TM is a soft thin biological tissue with small dimensions, using needles seems to be among the most practical interventional approaches. In this study, we proposed a finite-element (FE) analysis of needle-TM interactions that combines a 3D model of the TM and other main middle-ear structures in gerbil, and a 2D model of needle insertion into the TM based on the cohesive zone method (CZM). The TM was modelled using a 1 st -order Ogden hyperelastic material and its properties were obtained by fitting to the experimental force-displacement plots of large deformation in the TM under needle indentation. The cohesive parameters were also acquired by calibrating the puncture force against the experimental data of needle insertion into the TM. These FE models were then used to obtain the deformation behaviour of the TM and other middle-ear structures due to the insertion force applied at different locations on the TM. Moreover, we investigated the effect of the TM thickness, the geometry of the needle (i.e., diameter and tip angle), and needle material on the insertion of needles into the TM. We also studied the penetration success of deformable needles.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".