Demonstrating the efficacy at scale of a novel data-protecting AI system for surfacing clinically-eligible patients for clinical trials

大规模展示新型数据保护人工智能系统的功效，为符合临床条件的患者提供临床试验

• 批准号: 10018100
• 金额: $ 47.46万
• 依托单位: BITFOUNT LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10018100
• 关键词: Demonstrating; efficacy; scale; novel; data

**Issue****Clinical trial participant identification is desperately manual, time-consuming and frequently fails. As such, it is a well-known bottle-neck to healthcare R&D.****Clinical recruitment is a two-fold process: patient identification, followed by screening**. In order to identify patients with specific clinical presentation and attributes, pharmaceutical companies typically outsource to Contract Research Organisations (CRO) or Participant Identification Centres (PICs). CROs advertise widely, trawl through records manually and pay clinical experts to manually assess potential participant's fit. PICs and hospitals effectively wait for eligible patients to attend their consultations, whereupon inclusion in the trial is often forgotten! Consequently, patient identification using these inefficient methods takes months or years.**New Technologies**The last decade has seen an explosion in the use of machine learning and AI technologies across society. In the healthcare sector, these techniques have the potential to dramatically improve the quality and cost effectiveness of patient care. **This potential has been limited because healthcare data privacy is compromised in traditional machine learning**.A new development in AI is **federated-machine-execution**. Instead of moving data to a central location for machine learning models to be applied or trained, algorithms are sent to the data. For a hospital, this ensures the privacy of the data and provides control and a complete audit trail of its usage. **The hospital's data never leaves its secure environment**.**This project**This project uses **Bitfount Ltd's federated-machine-execution platform** to enable **Moorfields Eye Hospital's** **cutting-edge software models** (AI-biomarkers) to scan millions of medical images and identify potential trial participants from this primary, objective source **swiftly and accurately**, leaving patient data and comprehensive lists of clinically-eligible trial candidates inside the hospital database. Our approach promises to massively increase the number of clinically-eligible participants suitable for screening and trial recruitment through **fast, accurate, cheap identification, without data leaving the hospital**. Its efficiency and efficacy will be measured via a Study Within a Trial (SWAT) by **Surrey CTU**, experts in AI Clinical Trials.This project will demonstrate the approach in dry Age-related Macular Degeneration(AMD), which affects approximately 5% of Europeans aged 65 and over(39). Ophthalmology has the NHS' highest outpatient attendance rate(6) and AMD is by far the UK's leading cause of blindness(7), increasing with an ageing population. This project contributes to **NHSX's** digital transformation of ophthalmology, **BEIS' Grand Challenge for AI and data**(8), **DCMS' official Tech Priorities**(9), **UK's National AI Strateg**y(10) and **Life Sciences Industrial Strategy**(11).

**问题****临床试验参与者识别是拼命的，耗时且经常失败。因此，它是医疗保健研发的众所周知的瓶颈。为了确定具有特定临床表现和属性的患者，制药公司通常将研究组织（CRO）或参与者识别中心（PICS）外包。 CRO广泛宣传，手动浏览记录并支付临床专家，以手动评估潜在参与者的合适。图片和医院有效地等待符合条件的患者参加咨询，因此常常忘记参加试验！因此，使用这些低效方法的患者识别需要数月或数年的时间。在医疗保健领域，这些技术有可能显着提高患者护理的质量和成本效益。 **这种潜力受到限制，因为传统的机器学习中医疗数据隐私受到损害。将数据发送到要应用或训练的机器学习模型的中心位置，而是将算法发送到数据。对于医院，这确保了数据的隐私，并提供了控制和完整的审计途径。 **医院的数据永远不会离开其安全的环境**。医院数据库内的临床符合条件符合条件的试验候选人。我们的方法有望通过**快速，准确，廉价的身份识别，可以大大增加适合筛查和试用招募的临床资格参与者的数量，而不会离开医院**。它的效率和功效将通过** Surrey Ctu **的试验（SWAT）中的一项研究来衡量，AI临床试验的专家。该项目将证明与干燥年龄相关的黄斑变性（AMD）的方法，该方法影响了大约5％的65岁及以上（39）。 Ophthalmology具有NHS最高的门诊就诊率（6），而AMD是迄今为止英国失明的主要原因（7），随着人口老龄化的增长。该项目有助于** NHSX的**眼科数字化转型，** Beis对AI和数据的巨大挑战**（8），** DCMS的官方技术优先级**（9），**英国国家AI策略** Y（10）和**生活科学生命科学工业策略**（11）。