Next Generation Assessment of Fetal Wellbeing using Artificial Intelligence

使用人工智能进行下一代胎儿健康评估

• 批准号: MR/X029689/1
• 负责人: Manu Vatish
• 金额: $ 205.83万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 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%2FX029689%2F1
• 关键词: Next; Generation; Assessment; Fetal; Wellbeing

Electronic Fetal Heart monitoring (also called CTG) is the measurement of the fetal heart rate using probes that are placed on the mother's abdomen. It is the commonest test of fetal wellbeing worldwide (>200M tests per year are performed). It is used to try and assess how healthy the baby is and produces a readout that is very complex; surprisingly this readout is usually analyzed visually (by eye) and the clinician performing this analysis will use this to justify whether a baby needs to be delivered or not. There is a substantial amount of published data that shows this visual assessment is extremely poor and groups of clinicians disagree about a CTG and even the same doctor can interpret a CTG differently on different days. This means that some babies are delivered too soon and many sick babies are delivered too late - both create major problems for the babies, their parents, the NHS and society. CTG can be performed in pregnancy before labour or during labour. Most stillbirths occur before labour (>80%) and we have focused on this area.Many groups (including ourselves) have tried to standardize assessment of the CTG readouts using rudimentary computerised assessment. These systems certainly reduce the disagreements between clinicians but can't account for the multiple factors that make a CTG normal or abnormal (e.g. no systems account for the gestational age of a baby - e.g. treating a 28 week baby the same as a 38 week baby. These systems don't incorporate maternal or fetal disease into the analysis and none of these systems can tell clinicians what will happen to the baby in the coming days or weeks.We have brought together a team with expertise in CTG and artificial intelligence to deliver next generation assessment of the fetus.We will develop a suite of artificial intelligence based machine-learning models to revolutionise antepartum CTG analysis. Recent advances in deep-neural-networks (DNN) enable advanced analysis and identification of novel features within these complex signal patterns which we can exploit in conjunction with detailed maternal and fetal clinical outcomes to generate high fidelity diagnostic and prognostic tools. At our disposal is a unique unrivaled database of >165,000 fully classified CTG signals with associated maternal and neonatal outcome data from >56,000 pregnancies. Leveraging these data, we will develop artificial intelligence based technologies specific to the unique context of the mother and the fetus (at any gestational age). We have proven experience of analysing large datasets using these AI based tools. We have built into our plans the ability to validate our findings with prospective data from Oxford and Melbourne. The potential health benefits are substantial.Our work streams will allow us to generate clean data, generate tools that will allow our AI solution to be used on any CTG from any manufacturer. We will incorporate gestational age, maternal and fetal disease status and provide clinicians with a precise risk assessment of the fetus that will significantly improve the way we care for babies in the UK and beyond.

电子胎心监测（也称为CTG）是使用放置在母亲腹部的探头测量胎儿心率。它是全球最常见的胎儿健康测试（每年进行超过2亿次测试）。它被用来尝试和评估婴儿的健康状况，并产生非常复杂的读数;令人惊讶的是，这种读数通常是视觉分析（通过眼睛），执行这种分析的临床医生将使用它来证明婴儿是否需要分娩。有大量已发表的数据表明，这种视觉评估非常差，临床医生对CTG的看法不一致，甚至同一名医生在不同的日子对CTG的解释也不同。这意味着一些婴儿过早分娩，许多生病的婴儿分娩得太晚--这两种情况都给婴儿、他们的父母、NHS和社会带来了重大问题。CTG可在妊娠期、分娩前或分娩过程中进行。大多数死产发生在临产前（>80%），我们一直关注这一领域。许多小组（包括我们自己）试图使用基本的计算机评估来标准化CTG读数的评估。这些系统当然减少了临床医生之间的分歧，但不能考虑使CTG正常或异常的多个因素（例如，没有系统考虑婴儿的胎龄-例如，将28周的婴儿与38周的婴儿相同。这些系统不会将母亲或胎儿的疾病纳入分析，也无法告诉临床医生未来几天或几周内婴儿会发生什么。我们已经组建了一个拥有CTG和人工智能专业知识的团队，以提供下一代胎儿评估。我们将开发一套基于人工智能的机器学习模型，以彻底改变产前CTG分析。深度神经网络（DNN）的最新进展使我们能够对这些复杂信号模式中的新特征进行高级分析和识别，我们可以结合详细的母体和胎儿临床结果来开发高保真诊断和预后工具。在我们的处置是一个独特的无与伦比的数据库> 165，000完全分类的CTG信号与相关的孕产妇和新生儿的结果数据从> 56，000怀孕。利用这些数据，我们将开发基于人工智能的技术，专门针对母亲和胎儿（任何胎龄）的独特背景。我们拥有使用这些基于AI的工具分析大型数据集的经验。我们已经在我们的计划中建立了用牛津和墨尔本的前瞻性数据验证我们的发现的能力。潜在的健康益处是巨大的。我们的工作流程将使我们能够生成干净的数据，生成工具，使我们的人工智能解决方案能够用于任何制造商的任何CTG。我们将纳入胎龄，母亲和胎儿疾病状态，并为临床医生提供准确的胎儿风险评估，这将显着改善我们在英国及其他地区照顾婴儿的方式。