AI-powered portable MRI abnormality detection (APPMAD)

我们联合收割机结合了一系列的研究能力（新的便携式MRI扫描仪和我们的人工智能工具），并利用多学科和跨学科团队（来自不同院系和不同医院的临床医生和科学家以及具有相关生活经验的患者），进行早期转化医学研究，为患者带来潜在的益处。两项技术发展支持了拟议的工作。首先，我们开发了一种人工智能（AI）工具，该工具可以将磁共振成像（MRI）脑部扫描准确地分类为正常和异常（即，似乎有疾病）。我们构建的人工智能“分诊”工具允许放射科医生在正常扫描之前优先报告异常脑部扫描，从而更快地管理疾病患者。我们的人工智能工具的下游效应是减少疾病的影响和相关的医疗成本。其次，最新的技术允许使用小型便携式MRI扫描仪进行MRI扫描，不需要专门的医院房间和固定的MRI扫描仪。此外，与固定式MRI扫描不同，在金属制品旁边使用便携式MRI扫描仪也是安全的。因此，便携式MRI扫描仪可用于全科医生手术、社区诊断中心或推到重症监护室的患者床边，这些患者可能非常不适，因此转移到标准MRI部门的风险很高。与固定式MRI相比，便携式MRI扫描仪的购买和运行也非常便宜。“权衡”是获得的图像不如在固定MRI扫描仪中获得的扫描清晰。尽管如此，对于相对简单的任务，例如将患者分为正常和异常，图像的清晰度是足够的-如果异常，患者可以优先进行标准（固定）MRI，其中上级图像可以用于更复杂的评估。我们的目标是使用称为“迁移学习”的AI技巧，将联合收割机的知识从我们的AI工具中组合起来进行分类（这是为标准MRI扫描）与少量的研究扫描从便携式扫描仪，以建立一个准确的便携式MRI AI“分诊”工具。我们的建议将理所当然地提供所需的初步证据，以支持下一个转化研究步骤，将便携式MRI AI“分诊”工具带到临床。翻译的潜在用例相当多。由于将重症监护患者放入标准MRI扫描仪既危险又费力，因此配备AI“分诊”工具的床边便携式扫描仪可能会向治疗团队指示进行标准MRI是否明智和必要。此外，便携式扫描仪将允许在患者具有非特异性临床特征的社区进行初步分诊。例如，许多类型的头痛是一种常见的问题，但很少与异常有关。使用人工智能“分流”工具的社区分流将可能允许更快速地对异常患者进行靶向成像，例如，对可能的脑肿瘤患者进行专门的MR成像。该研究具有巨大的潜力，可为英国等国家的医院和社区医学做出贡献。我们还强调，几乎无法获得标准MRI的低收入国家可能会从这种工具中受益不成比例，因为便携式扫描仪很便宜（约20万英镑，而标准MRI扫描仪约1- 200万英镑）。

We combine a range of research capabilities (new portable MRI scanner and our AI tools), and draw on multi- and interdisciplinary teams (clinicians and scientists from different faculties and different hospitals as well as a patient with relevant lived experience), to conduct early-stage translational medical research with the potential for patient benefit.Two technological developments underpin the proposed work.First, we developed an Artificial Intelligence (AI) tool that can accurately sort magnetic resonance imaging (MRI) brain scans into normal and abnormal (i.e., there appears to be disease). The AI "triage" tool that we built allows radiologists to report abnormal brain scans preferentially before normal scans which results in faster management of patients with disease. The downstream effect of our AI tool is to reduce the effects of disease, and related healthcare costs.Second, recent technology allows MRI scans to be performed using a small, portable MRI scanner that does not require a dedicated hospital room with a fixed MRI scanner. Furthermore, unlike fixed MRI scans, it is also safe to use the portable MRI scanner next to metalwork. As such the portable MRI scanner can be used in GP surgeries, Community Diagnostic Hubs or wheeled to the bedside of a patient in an Intensive Care Unit who may be very unwell and therefore at high risk for transfer to the standard MRI department. The portable MRI scanner is also very cheap to buy and to run when compared to a fixed MRI. The "trade off" is that the images obtained are not as clear as the scans obtained in fixed MRI scanners. Nonetheless, the clarity of the images for relatively simple tasks such as sorting patients into normal and abnormal is sufficient - if abnormal, patients can be prioritised for onward referral for standard (fixed) MRI where the superior images can be used for more complex assessments.Our aim is to use an AI trick called "transfer learning" to combine knowledge from our AI tool for triage (which was built for standard MRI scans) with a small number of research scans from the portable scanner in order to build an accurate portable MRI AI "triage" tool.Our proposal will plausibly provide the initial evidence required to support the next translational research step that would bring the portable MRI AI "triage" tool to the clinic. Potential use-cases for translation are considerable. Because putting an intensive care patient inside a standard MRI scanner is both hazardous and laborious, a bedside portable scanner with an AI "triage" tool might indicate to the treating team whether it is sensible and necessary to proceed to standard MRI.Additionally, the portable scanner would allow an initial triage in the community where patients have nonspecific clinical features. For example, many types of headache are a common problem but rarely associated with an abnormality. Community triage with an AI "triage" tool would plausibly allow more rapid onward referral for targeted imaging in those with an abnormality, for example, specialised MR imaging for possible brain tumour patients.The research has immense potential to contribute to hospital and community medicine in countries like the UK. We also emphasise that low-income countries with almost no access to standard MRIs might benefit disproportionately from such a tool, as the portable scanner is cheap (~£200k compared to ~£1-2M for a standard MRI scanner).