Machine learning model for automated calculation of intracochlear positional index in cochlear implantation.
Authors
Affiliations (6)
Affiliations (6)
- Department of Otolaryngology-Head & Neck Surgery, Royal Children's Hospital, Parkville, VIC, 3052, Australia. [email protected].
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, 2050, Australia. [email protected].
- School of Computer Science, University of Sydney, Camperdown, NSW, 2050, Australia.
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, 2050, Australia.
- Department of Otolaryngology-Head & Neck Surgery, Royal Children's Hospital, Parkville, VIC, 3052, Australia.
- Department of Otolaryngology-Head & Neck Surgery, Westmead Hospital, Westmead, NSW, 2145, Australia.
Abstract
Training and refining both custom and pre-trained convolutional neural network (CNN) models for calculation of intracochlear positional index (ICPI) is as effective as manual calculation. The ICPI is a position factor that is known to influence cochlear implant performance, however manual calculation on computed tomography (CT) imaging is labour-intensive and prone to calculation errors. Automation of this process with machine learning via a custom built CNN model aims to reduce the difficulty in obtaining this position factor. Increasing the number of training epochs will improve accuracy. Our study aims to develop a validated CNN for ICPI calculation, which may improve surgical electrode positioning. Custom built CNN model and pre-trained ResNet 50 model trained and validated on 34 images, and tested on eight CT images of temporal bones with cochlear implants. The ground truth was manually established by calculating the distance from modiolus to electrode (DE) and lateral wall (DL), and applied to derive the ICPI. The pre-trained ResNet-50 model outperformed the custom-built CNN, with improvement statistically significant on evaluation metrics. The ResNet-50 model has lower mean absolute error and root mean squared error (RMSE). In both models, increasing the number of training epochs from ten to 100 improves accuracy of the ICPI calculation. Our machine learning models successfully achieved automation of ICPI calculation, with increasing accuracy increasing training epochs to 100 iterations. Future studies should explore optimizing these models and validating them on broader datasets to enhance their applicability in real-world scenarios by comparison to speech and audiometric outcomes.