Real-time intracochlear imaging of automated cochlear implant insertions in whole decalcified cadaver cochleas using ultrasound
Bibliographic record
Abstract
OBJECTIVES: This study aimed to determine the feasibility of combining high-frequency ultrasound imaging, automated insertion, and force sensing to yield more information about cochlear implant insertion dynamics. METHODS: An apparatus was developed combining these aspects along with software to control implant and imaging probe positions. Decalcified unfixed human cochleas were implanted at various speeds, insertion sites, and implant models while imaging near the implant tip throughout insertion and recording force data from the cochlea mounting stage. Ultrasound video data were also captured. RESULTS: The basilar membrane (BM) was frequently penetrated by the implant in either the mid-basal or lower middle turn. Measurements were also performed of apical BM motion in response to upstream implant movement at varying insertion speeds. Increasing insertion speed resulted in greater BM displacement. DISCUSSION: Multiple insertions per cochlea increase the volume of data per specimen while also reducing variability due to differences between cochleas. However, to image inside the cochlea with ultrasound, the bone had to be decalcified, which likely had a significant effect upon the response of tissue to contact by the implant. As calcified bone strongly reflects ultrasound, we also found ultrasound imaging to be an excellent method for easily assessing bone decalcification progress. CONCLUSION: This technique may be very useful for some studies, although the confounding effects of bone decalcification may make results of other studies too difficult to generalize. The approach could be adapted to other real-time imaging modalities, such as optical coherence tomography.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".