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

**问题*临床试验参与者识别非常费时费力，而且经常失败。因此，这是众所周知的医疗研发的瓶颈*临床招聘是一个双重过程：确定患者身份，然后进行筛查**。为了识别具有特定临床表现和特征的患者，制药公司通常外包给合同研究组织(CRO)或参与者识别中心(PIC)。CRO广泛做广告，手动查阅记录，并付钱给临床专家，让他们手动评估潜在参与者的适合度。PIC和医院有效地等待符合条件的患者参加他们的会诊，因此试验中的纳入往往被遗忘！因此，使用这些低效方法识别患者需要几个月或几年的时间。**新技术**在过去十年里，机器学习和人工智能技术在整个社会的使用出现了爆炸性增长。在医疗保健领域，这些技术有可能显著提高患者护理的质量和成本效益。**这一潜力受到了限制，因为医疗数据隐私在传统机器学习中受到了损害**。人工智能的新发展是**联合机器执行**。不是将数据移动到中央位置，以便应用或训练机器学习模型，而是将算法发送到数据。对于医院来说，这确保了数据的隐私，并提供了对其使用情况的控制和完整的审计跟踪。**医院的数据永远不会离开它的安全环境**。**本项目**本项目**使用**BitFunt有限公司的联合机器执行平台**，使**Moorfield眼科医院的*尖端软件模型**(AI-BioMarkers)能够扫描数百万张医学图像，并从这个主要的、客观的来源**快速准确地**识别潜在的试验参与者**，将患者数据和符合临床条件的试验候选人的全面列表留在医院数据库中。我们的方法承诺通过**快速、准确、廉价的身份识别**大幅增加符合临床条件的参与者的数量，适合进行筛查和试验性招募，而无需将数据传出医院**。它的有效性和有效性将通过人工智能临床试验专家**萨里·CTU**进行的一项试验中研究(SWAT)来衡量。该项目将展示该方法在干性年龄相关性黄斑变性(AMD)中的应用，该疾病影响约5%65岁及以上的欧洲人(39岁)。眼科的门诊率是英国国民健康保险制度中最高的(6)，AMD是英国目前为止导致失明的主要原因(7)，随着人口老龄化，AMD的发病率也在增加。该项目为**NHSX的**眼科数字化转型，**BEIS的AI和数据大挑战**(8)，**DCMS的官方技术重点**(9)，**英国的国家AI战略**(10)和**生命科学产业战略**(11)做出了贡献。

**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).