AI driven inhalation technique and adherence support device with data aggregation platform for remote monitoring of asthma

人工智能驱动的吸入技术和带有数据聚合平台的依从性支持装置，用于远程监测哮喘

• 批准号: 56551
• 金额: $ 40.63万
• 依托单位: RESPIRATORY ANALYTICS LTD
• 依托单位国家: 英国
• 项目类别: Study
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=56551
• 关键词: AI; driven; inhalation; technique; adherence

The global cost of asthma is high and rising with an expected 400M sufferers by 2025 and 1000 deaths every day. The UK has one of the highest prevalence and mortality rates in Europe. Over 5.4 million people in the UK have this complex condition which is often difficult to manage. Around 1400 people die each year in the UK and up to 70% of these deaths are preventable with better symptom control. In the UK alone costs are over £1.1B.Uncontrolled asthma remains a stubbornly intractable problem. Outcomes have plateaued in the last 20 years despite major pharma investment in new drugs and costly drug delivery technologies and Metered Dose Inhalers (MDIs) remain the mainstay of treatment. Symptom control by inhaler devices can be adversely impacted by incorrect inhaler technique and poor adherence. Innovation has focused on Apps and 'smart inhalers' that prompt adherence (not inhalation technique), and monitor inhaler usage to try to improve symptom control.Evidence of benefit from these first generation smart inhalers has been limited and reviews point to the need for a second generation to deliver clinical, physiological and behavioural data insights including the identification of inhalation technique and environmental interactions, to improve self management and ensure optimum symptom control. The latest smart inhalers and platforms deliver _some_ of this functionality but fail to identify, integrate and correct the critical steps of correct inhalation to support self management.Building on the expertise gained building a previous CE marked inhalation device, our proposal will give detailed, real-time visualisation of each inhalation step, _for the first time._ Our R&D will deliver a cloud based, AI driven data aggregation and analytics platform to permit real time reporting to a new App and healthcare professional (HCP) dashboard, generate risk notifications and test in a synthetic population, ready for UK clinical trials.The application of Machine Learning algorithms to the aggregated clinical data, air quality measurements, weather and epidemiology data, combined with clinical expertise, will support hyper personalised self management via data insights and identify 'at risk' patients for HCP intervention. Delivering these real-time data insights to users and HCPs will reduce the cost burden of avoidable HCP visits and hospital admissions.Throughout the duration of the research we will work with closely with Asthma UK, clinicians and user focus groups to refine the design of the product and the app in an iterative way, to ensure user acceptability.

全球哮喘的成本很高，而且还在上升，预计到2025年将有4亿患者，每天有1000人死亡。英国是欧洲发病率和死亡率最高的国家之一。英国有超过540万人患有这种复杂的疾病，通常很难控制。英国每年约有1400人死亡，其中高达70%的死亡是可以通过更好的症状控制来预防的。仅在英国，治疗费用就超过11亿英镑。哮喘失控仍然是一个顽固的棘手问题。在过去的20年里，尽管制药公司对新药和昂贵的药物输送技术进行了重大投资，但结果已经趋于稳定，计量吸入器（MDI）仍然是治疗的支柱。吸入器技术不正确和依从性差可能会对吸入器装置的症状控制产生不利影响。创新的重点是应用程序和“智能吸入器”，以促进遵守这些第一代智能吸入器的益处证据有限，审查指出需要第二代智能吸入器提供临床、生理和行为数据见解，包括吸入技术和环境相互作用的识别，改善自我管理，确保最佳的症状控制。最新的智能吸入器和平台提供了一些这种功能，但未能识别，整合和纠正正确吸入的关键步骤，以支持自我管理。基于构建以前CE认证的吸入设备所获得的专业知识，我们的提案将首次提供每个吸入步骤的详细，实时可视化。我们的研发将提供一个基于云的人工智能驱动的数据聚合和分析平台，允许真实的时间报告到一个新的应用程序和医疗保健专业人员（HCP）仪表板，生成风险通知和测试在一个合成的人口，准备英国的临床试验。机器学习算法的应用，以聚合的临床数据，空气质量测量，天气和流行病学数据，结合临床专业知识，将通过数据洞察支持超个性化的自我管理，并识别“高危”患者进行HCP干预。向用户和HCP提供这些实时数据见解将减少可避免的HCP访问和住院的成本负担。在整个研究期间，我们将与Asthma UK，临床医生和用户焦点小组密切合作，以迭代的方式完善产品和应用程序的设计，以确保用户的可接受性。