Title: Development of an Artificial Intelligence System for the Automated Diagnosis of Otitis Media with Effusion in Children
Funder: Department of Health and Social Care

Abstract: Background Wideband Absorbance Immittance (WAI) has been shown to be effective in detecting conductive hearing loss when compared to conventional low frequency tympanometry. However, the main challenge for clinicians is that the WAI data contains thousands of values at different pressures and frequency regions. Our recent study used machine learning tools to better understand and interpret WAI results, which provides an important stepping stone to maximise its clinical application as an effective diagnostic tool in ENT/Audiology clinics. Overall aim The overall aim is to use advanced Deep Learning (DL) techniques to develop methodologies for the accurate and reliable automated diagnosis of childhood OME. The specific objectives are: Objective1: To further develop and evaluate DL models using advanced techniques to train classification models efficiently to improve their performance; Objective2 : To develop AI-WAI interface software for the automated diagnosis of childhood OME; Objective3: To conduct clinical trials to verify the DL model and AI-WAI interface software for automatically diagnosing childhood OME; Objective4: To disseminate a proof of concept, and facilitate knowledge transfer and commercial exploitation; Objective5 : To propose a strategic plan for developing an effective model hearing healthcare in childhood OME. Work plan and Timelines for delivery We propose a comprehensive work plan, starting from July 2021 for 12 months. We will undertake five packages to achieve the objectives highlighted above. The timelines for delivery and milestones of this project are shown in the Gantt chart. Anticipated Impact and Dissemination Due to the technological innovation, there will be significant and direct impacts on clinical assessment and diagnostic concepts for childhood OME in professional communities, and thus NICE guidelines for childhood OME treatment. Dissemination to professional communities and the wider community to enhance stakeholder engagement and patient and public involvement. Further dissemination with the global hearing industry will enhance business partnerships and collaborations.

Lay abstract: Aim The project aim is to use advanced Artificial Intelligence (AI) techniques to develop methodologies for the accurate and reliable automatic diagnosis of otitis media with effusion (OME). Background OME (also known as Glue Ear ) is one of the most common causes of childhood hearing impairment and disability. It is estimated that more than 80% of children will have otitis media before the age of 10. This places a significant cost burden on the NHS with approximately 200,000 children with OME seen annually in primary care. Delayed diagnosis and poorly managed cases can result in severe and persistent OME with surgical treatment becoming the only management option, leading to long waiting times, and excessively high costs for the NHS. Our recent research into AI has made great progress in demonstrating its potential for the accurate diagnosis of OME. This novel research proves the capability of AI tools to diagnose OME automatically with an accuracy of 82%. We aim to improve accuracy and reliability further using advanced AI tools in the proposed project. Work plan To achieve the research objectives, we will undertake five work packages (WP): WP1: Advanced AI (i.e. deep learning) model innovation; WP2: AI interface software development; WP3: Clinical trials for model verification; WP4: Proof of Concept development and knowledge transfer and, WP5: Outline plans of cost-effective research. The WPs will incorporate advanced techniques to efficiently train powerful AI models from available small datasets. We will integrate the built AI model with middle ear measurement software to provide a diagnostic decision with a value of confidence level in the decision. Clinical trials for model verification will be conducted, and patent applications for the deep learning packages and interface software will be submitted. Knowledge transfer of the innovative developments will be discussed with business partners. Patient and public involvement (PPI) We intend to promote the work publicly to the widest possible audience. Parents of children with OME have already discussed with us their concerns and personal experiences, and identified important directions for future research. Mr Henry Yeomans, PPI co-applicant will lead a PPI team to review the clinical trials and facilitate public engagement. Dissemination and anticipated outcomes We will disseminate our work to the professional community by publishing scientific reports, organising seminars and attending international conferences. In addition, we shall share supportive information with parents on OME diagnosis and decision making through social media and networks (e.g., National Deaf Children s Society). We anticipate that significant outcomes will improve diagnostic accuracy, and reduce the NHS cost burden by preventing 'delayed' or unnecessary referrals, cutting down waiting time for children with OME, and contributing towards improving the capacity of ENT/Audiology specialists who can then provide more efficient and cost-effective services.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: EAR