Tissue Classification After Bone-Anchored Hearing Implant Surgery: A Machine Learning Approach to Monitoring Skin Response.

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.