AI-powered portable MRI abnormality detection (APPMAD)

我们结合了一系列研究能力（新型便携式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），开展有可能造福患者的早期转化医学研究。两项技术发展为拟议的工作提供了基础。首先，我们开发了一种人工智能（AI）工具，可以准确地将磁共振成像（MRI）脑部扫描分为正常和异常（即似乎有疾病）。我们开发的人工智能“分诊”工具允许放射科医生在正常扫描之前优先报告异常的脑部扫描，从而更快地治疗患者。我们的人工智能工具的下游效应是减少疾病的影响，以及相关的医疗成本。其次，最近的技术允许使用小型便携式MRI扫描仪进行MRI扫描，而不需要配备固定MRI扫描仪的专用病房。此外，与固定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病房的病人床边，这些病人可能非常不舒服，因此转移到标准MRI部门的风险很高。与固定的核磁共振成像相比，便携式核磁共振成像扫描仪购买和运行起来也非常便宜。“折衷”是，获得的图像不像在固定MRI扫描仪中获得的扫描那样清晰。尽管如此，对于相对简单的任务，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，患者可以优先转诊到标准（固定）MRI，在那里，优越的图像可以用于更复杂的评估。我们的目标是使用一种称为“迁移学习”的人工智能技巧，将我们的人工智能分诊工具（为标准MRI扫描而构建）的知识与便携式扫描仪的少量研究扫描相结合，以构建准确的便携式MRI人工智能“分诊”工具。我们的建议将提供初步证据，以支持下一个转化研究步骤，将便携式MRI人工智能“分诊”工具带入临床。翻译的潜在用例是相当多的。由于将重症监护患者放入标准的MRI扫描仪中既危险又费力，因此带有人工智能“分诊”工具的床边便携式扫描仪可能会向治疗团队表明是否明智且有必要进行标准MRI检查。此外，便携式扫描仪将允许在患者有非特异性临床特征的社区进行初步分类。例如，许多类型的头痛是一个常见的问题，但很少与异常有关。使用人工智能“分诊”工具的社区分诊似乎可以让那些有异常的人更快地转介到有针对性的成像，例如，为可能的脑肿瘤患者进行专门的磁共振成像。这项研究对像英国这样的国家的医院和社区医学有巨大的潜力。我们还强调，几乎无法获得标准核磁共振成像的低收入国家可能会从这种工具中获得不成比例的好处，因为便携式扫描仪便宜（约20万英镑，而标准核磁共振成像扫描仪约为120万英镑）。

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).