Tissue Classification After Bone-Anchored Hearing Implant Surgery: A Machine Learning Approach to Monitoring Skin Response
Bibliographic record
Abstract
HYPOTHESIS: Here, we aim to 1) expand the available evidence for the use of machine learning techniques for soft tissue classification after BCD surgery and 2) discuss the implications of such approaches toward the development of classification applications to aid in tissue monitoring. BACKGROUND: The application of machine learning techniques in the soft tissue literature has become a large field of study. One of the most commonly reported outcomes after percutaneous bone-conduction device (BCD) surgery is soft tissue health. Unfortunately, the classification of tissue around the abutment as healthy versus not healthy is a subjective process, even though such decisions can have implications for treatment (i.e., topical steroid versus surgical revision) and resources (e.g., clinician time). METHODS: We built and tested a convolutional neural network (CNN) model for the classification of tissues that were rated as "green" (i.e., healthy), "yellow" (i.e., unhealthy minor), and "red" (i.e., unhealthy severe). METHODS: Representative image samples were gathered from a regional bone-conduction amplification site (N = 398; 181 samples of green; 144 samples of yellow; 73 samples of red). The image samples were cropped, zoomed, and normalized. Feature extraction was then implemented and used as the input to train an advanced CNN model. RESULTS: Accuracy of image classification for the healthy ("green") versus not healthy ("yellow" and "red") model was approximately 87%. Accuracy of image classification for the unhealthy ("yellow") versus unhealthy ("red") model was approximately 94%. CONCLUSIONS: Monitoring tissue health is an ongoing challenge for BCD users and their clinicians not trained in soft tissue management (e.g., audiologists). If machine learning can aid in the classification of tissue health, this would have significant implications for stakeholders. Here we discuss how machine learning can be applied to tissue classification as a potential technological aid in the coming years.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".