Understanding hearing loss phenotypes, their progression and associations with otological and non-otological disease using hearing health big data

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Aims and Background to the researchHearing loss is the most common sensory disorder in humans with 1.5 billion people affected by this during their lifetime. Some types of hearing loss are more common as we get older and therefore the burden of hearing loss is predicted to rise further as our aging population increases. Hearing loss has a significant impact on quality of life, patient health and safety as well as placing a huge demand on increasingly stretched public health services.In order to prepare and respond to this rapidly accelerating public health crisis we need to better understand the different types of hearing loss to identify who is most at risk of both worsening hearing loss but also other associated medical conditions and also which patients are most likely to benefit from new treatments. The digitialisation of patient health records offers an exciting opportunity to use the latest advances in computer science methods to look at large amounts of hearing health patient data and answer these questions.Another key part of this project will look at clinical photographs of the ear drum. Access to trained specialists who can assess the appearance of ear drums is limited in the community and there are often long waits for referrals to specialty ENT services. This situation is even worse globally in resource-poor countries. To address this problem, we propose to develop an automated programme that analyse photographs of the ear drum. The clinical images will also be used to assess whether changes in the ear drum could signal the presence of vascular disease and diabetes, much like retinal screening is performed in the eye. The ear is a more readily accessible area than the eye and could provide an easy and cost-effective site for screening.MethodsWe will create a store of patient data that has been collected routinely as part of standard NHS clinical care. This will include demographic details, test results, measurements, and details of medical conditions. A powerful computer programme will be used to analyse this data and describe different types of hearing loss as well as how these hearing loss types change over time. We will perform further analysis to identify links between these hearing loss subtypes and other medical conditions including dementia, diabetes, stroke and high blood pressure.For the second part of this study, we will use pictures of ear drums captured by a new medical device to develop and train a computer programme that can identify and grade the key components of an ear drum that are assessed by ENT specialists. We will use this programme alongside supplied patient details to explore whether there are changes in the ear drum that can predict the presence of diabetes and heart disease.Anticipated OutcomesThe key aim of this research is to better understand the natural history of hearing loss. Identifying patients who are at higher risk of developing severe hearing loss is important for resource planning, patient counselling and identifying people who are most likely to benefit from emerging treatments or clinical trials. Identifying new associations between hearing loss and other conditions could identify patients who are "at risk" prompting earlier diagnosis and act as an opportunity for early intervention and the promotion of lifestyle modifications to divert or delay the onset of such conditions through behavioural change.