Using artificial intelligence to screen children suspected with listening difficulties

The goal of this project is to create a cutting-edge screening tool for children with listening difficulties, which can have a profound impact on their academic performance, social development, and overall well-being. Despite the current assessment process, which is time-consuming and requires extensive training and experience, early identification and intervention are critical for these children to reach their full potential.

Artificial Intelligence (AI) and Machine Learning (ML) algorithms have the potential to revolutionize healthcare by providing more information to inform diagnoses and improve diagnostic accuracy and efficiency. However, applying AI/ML to medical applications can be challenging due to the complexity and variability of medical data.

The project team has previous experience in developing fast and accurate infant ABR screening algorithms, which achieved 96% accuracy in detecting ABRs. In this project, the team will use deep learning approaches to create a fast and accurate AI-based screener for children with listening difficulties. The developed screener will be tested for accuracy, efficiency, and usefulness compared to the current assessment process. The work will be carried out in collaboration with Vivosonic Inc., a leading company in the field of hearing screening technologies.

By successfully completing this project, the team will contribute to filling the gap in research on developing efficient and reliable screening tools for children with listening difficulties and improving early identification and intervention. This project has the potential to improve the lives of many children by giving them a better chance to reach their full potential.

Faculty Supervisor:

Soodeh Nikan;Prudence Allen;Prudence Allen

Student:

Partner:

Vivosonic Inc.

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

The University of Western Ontario

Program: