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

使用听力健康大数据了解听力损失表型、其进展以及与耳科和非耳科疾病的关联

• 批准号: MR/X019217/1
• 负责人: Lilia Dimitrov
• 金额: $ 36.76万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX019217%2F1
• 关键词: Understanding; hearing; loss; phenotypes; their

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.

研究性损失的目的和背景是人类中最常见的感觉障碍，其中15亿人在其一生中受到影响。随着年龄的增长，某些类型的听力损失更为普遍，因此随着人口老龄化的增加，听力损失的负担预计会进一步增加。听力损失对生活质量，患者健康和安全以及对日益拉伸的公共卫生服务的巨大需求产生了重大影响。为了准备并应对这种快速加速的公共卫生危机，我们需要更好地了解不同类型的听力损失，以确定谁最有可能恶化的听力损失以及其他相关的医疗状况以及哪些患者以及哪些患者最有可能受益于新治疗。患者健康记录的数字化为使用计算机科学方法的最新进展提供了一个令人兴奋的机会，以查看大量的听力健康患者数据并回答这些问题。该项目的另一个关键部分将介绍耳鼓的临床照片。在社区中，可以评估可以评估耳鼓外观的受过训练的专家的访问限制，并且通常需要长时间等待推荐专业服务。在资源贫乏的国家，这种情况在全球范围内甚至更糟。为了解决这个问题，我们建议开发一个自动化程序，以分析耳鼓的照片。临床图像还将用于评估耳鼓中的变化是否可以表明存在血管疾病和糖尿病的存在，就像在眼中进行视网膜筛查一样。耳朵比眼睛更容易进入，并且可以提供一个简单且具有成本效益的筛查站点。Methodswe将创建一系列患者数据存储，作为标准NHS NHS临床护理的一部分，该数据已定期收集。这将包括人口统计细节，测试结果，测量以及医疗状况的细节。强大的计算机程序将用于分析这些数据并描述不同类型的听力损失以及这些听力损失类型如何随时间变化。我们将进行进一步的分析，以确定这些听力损失亚型与其他医疗状况（包括痴呆症，糖尿病，中风和高血压）之间的联系。对于本研究的第二部分，我们将使用新医疗设备捕获的耳鼓的图片来开发和训练计算机程序，该程序可以识别并识别专家评估EAN DRUM的关键成分，并由专家评估了ENN专家的关键成分。我们将与提供的患者详细信息一起使用该程序，以探索耳鼓中是否有可以预测糖尿病和心脏病的存在的变化。期待这项研究的主要目的是更好地了解听力丧失的自然历史。确定患有严重听力损失风险的患者对于资源计划，患者咨询以及确定最有可能受益于新兴治疗或临床试验的人很重要。确定听力损失与其他条件之间的新关联可以确定那些处于“处于危险之中”的患者，促使早期诊断并充当早期干预的机会，并促进生活方式修改以通过行为改变转移或延迟这种状况的发作。