Transtympanic Visualization of Cochlear Implant Placement With Optical Coherence Tomography: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the ability of transtympanic middle ear optical coherence tomography (ME-OCT) to assess placement of cochlear implants (CIs) in situ. PATIENT: A 72-year-old man with bilateral progressive heredodegenerative sensorineural hearing loss due to work-related noise exposure received a CI with a slim modiolar electrode for his right ear 3 months before his scheduled checkup. INTERVENTION: A custom-built swept source ME-OCT system (λo = 1550 nm, ∆λ = 40 nm) designed for transtympanic middle ear imaging was used to capture a series of two- and three-dimensional images of the patient's CI in situ. Separately, transtympanic OCT two-dimensional video imaging and three-dimensional imaging were used to visualize insertion and removal of a CI with a slim modiolar electrode in a human cadaveric temporal bone through a posterior tympanotomy. MAIN OUTCOME MEASURE: Images and video were analyzed qualitatively to determine the visibility of implant features under ME-OCT imaging and quantitatively to determine insertion depth of the CI. RESULTS: After implantation, the CI electrode could be readily visualized in the round window niche under transtympanic ME-OCT in both the patient and the temporal bone. In both cases, characteristic design features of the slim modiolar electrode allowed us to quantify the insertion depth from our images. CONCLUSIONS: ME-OCT could potentially be used in a clinic as a noninvasive, nonionizing means to confirm implant placement. This study shows that features of the CI electrode visible under ME-OCT can be used to quantify insertion depth in the postoperative ear.
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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 | 0.002 |
| 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 | 0.001 |
| 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".