Understanding hearing loss phenotypes, their progression and associations with otological and non-otological disease using hearing health big data
Lead Research Organisation:
UCL Hospitals NHS Foundation Trust
Department Name: ENT Surgical Department
Abstract
Aims and Background to the research
Hearing 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.
Methods
We 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 Outcomes
The 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.
Hearing 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.
Methods
We 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 Outcomes
The 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.
Technical Summary
Background
Hearing loss is the most common sensory disorder in humans and its prevalence is predicted to grow with an increasing ageing population. In order to prepare and respond to this rapidly accelerating public health crisis it is imperative that we robustly characterise hearing loss phenotypes including risk factors, progression and associations with other diseases. Electronic Health Records (EHRs) provide an invaluable resource where machine learning methods can be applied to large hearing health datasets to address these issues.
Aims
This doctoral thesis proposal aims to:
1. characterise data-driven hearing loss phenotypes in order to prognosticate patients and explore the associations between different hearing loss phenotypes with both otological and non-otological outcomes
2. automate the segmentation of otoscopic clinical images in order to develop a scalable and accurate system that can be used to assist in settings with limited access to ear and hearing health specialists as well as identify whether visible changes in the ear drum can be used to screen for diabetes and vascular disease.
Methods
We will enrich a pre-existing hearing health relational database through data linkage across 3 different databases to include routinely captured hearing health data from electronic health records (EHRs) including patient demographics, clinical codes and biomarkers. The SuStaIn algorthim will be applied to this dataset to identify distinct hearing loss phenotypes across subtype and stage and advanced statistical and data science methods will be used to explore the association between different hearing loss phenotypes and patient outcomes.
Finally, we will develop and train a segmentation algorithm on clinical images captured through a novel medical device to isolate key areas of the ear drum and create a grading system. Through association studies we will explore whether these features and their grades are associated with other clinical diagnoses.
Hearing loss is the most common sensory disorder in humans and its prevalence is predicted to grow with an increasing ageing population. In order to prepare and respond to this rapidly accelerating public health crisis it is imperative that we robustly characterise hearing loss phenotypes including risk factors, progression and associations with other diseases. Electronic Health Records (EHRs) provide an invaluable resource where machine learning methods can be applied to large hearing health datasets to address these issues.
Aims
This doctoral thesis proposal aims to:
1. characterise data-driven hearing loss phenotypes in order to prognosticate patients and explore the associations between different hearing loss phenotypes with both otological and non-otological outcomes
2. automate the segmentation of otoscopic clinical images in order to develop a scalable and accurate system that can be used to assist in settings with limited access to ear and hearing health specialists as well as identify whether visible changes in the ear drum can be used to screen for diabetes and vascular disease.
Methods
We will enrich a pre-existing hearing health relational database through data linkage across 3 different databases to include routinely captured hearing health data from electronic health records (EHRs) including patient demographics, clinical codes and biomarkers. The SuStaIn algorthim will be applied to this dataset to identify distinct hearing loss phenotypes across subtype and stage and advanced statistical and data science methods will be used to explore the association between different hearing loss phenotypes and patient outcomes.
Finally, we will develop and train a segmentation algorithm on clinical images captured through a novel medical device to isolate key areas of the ear drum and create a grading system. Through association studies we will explore whether these features and their grades are associated with other clinical diagnoses.
