AI4RA: AI-powered visual technology to diagnose rheumatoid arthritis activity

AI4RA：人工智能驱动的视觉技术用于诊断类风湿性关节炎活动

• 批准号: 75908
• 金额: $ 10.94万
• 依托单位: IMPERIAL COLLEGE LONDON
• 依托单位国家: 英国
• 项目类别: Study
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=75908
• 关键词: AI4RA; AI; powered; visual; technology

The AI4RA project will enable the secondment of Dr Wan Rusli from Imperial College to Arthronica. The objective of the secondment is to employ Dr Rusli's skills in biomechanics and computational modelling to evaluate Arthronica's ability to provide remote monitoring and determine disease activity status for patients with rheumatoid arthritis (RA).RA is a chronic, disabling autoimmune condition in which the body attacks the cells that line the joints, making the joints swollen, stiff and painful; over time this can also damage the cartilage and nearby bone. The National Audit Office estimates that approximately 580,000 adults in England currently have the disease, with a further 26,000 new cases diagnosed each year. There is growing evidence to suggest that treatment within 12 weeks is associated with improved response to treatment and patient outcomes. This is further supported by a number of studies that have shown that the best clinical outcomes are achieved through a treat-to-target approach. This requires that patients receive at least bi-monthly follow-ups in order to determine if their disease is active or the treatment has successfully dampened disease activity.The leading metric to assess disease activity in RA is the Disease Activity Score using 28 joints (DAS-28). The DAS-28 is routinely measured in clinic visits for patients with RA. It involves four domains: a clinician-reported swollen joint count, a clinician-reported tender joint count, a patient global measure of symptoms, and a biomarker of inflammation from a blood test. In the era of the coronavirus pandemic, it is overtly apparent that we need innovative solutions that enable measurement of disease activity remotely, but still reliably. Biomarker levels can be obtained, as home testing kits are available. Patient symptom severity scores are easily captured. Joint tenderness can be learnt through patient self-assessment. What is needed is a mechanism for remotely recording the status of the joints, including swelling and range of motion.To address these needs, Arthronica is engaged in a cross-sectional diagnostic accuracy study with the Rheumatology Department at King's College Hospital to record images of and measure the swollen joints. However, the company is lacking in-depth expertise in biomechanical modelling. Dr Rusli has extensive experience in computational modelling and musculoskeletal biomechanics and will support Arthronica researchers in the validation of the RA biomechanical model to increase the technology readiness for remotely assessing RA disease activity.

AI4RA项目将使万鲁斯利博士能够从帝国理工学院借调到阿瑟罗尼察。借调的目的是利用Rusli博士在生物力学和计算建模方面的技能来评估Arthronica为类风湿性关节炎(RA)患者提供远程监控和确定疾病活动状态的能力。RA是一种慢性、致残的自身免疫性疾病，身体攻击关节内的细胞，使关节肿胀、僵硬和疼痛；随着时间的推移，这也会损害软骨和附近的骨骼。国家审计署估计，目前英国约有58万成年人患有这种疾病，每年还会有2.6万新确诊病例。越来越多的证据表明，12周内的治疗与对治疗的反应和患者结果的改善有关。许多研究进一步支持了这一点，这些研究表明，通过靶向治疗的方法可以获得最佳的临床结果。这要求患者至少每两个月接受一次随访，以确定他们的疾病是否活动，或者治疗是否成功地抑制了疾病的活动。评估RA疾病活动的主要指标是使用28个关节的疾病活动评分(DAS-28)。DAS-28在RA患者的临床就诊中进行常规测量。它涉及四个领域：临床医生报告的肿胀关节计数，临床医生报告的压痛关节计数，患者全球症状的测量，以及血液测试中的炎症生物标志物。在冠状病毒大流行的时代，显而易见的是，我们需要创新的解决方案，能够远程测量疾病活动，但仍然可靠。可以获得生物标志物水平，因为有家庭检测试剂盒可用。患者的症状严重程度分数很容易获取。关节压痛可以通过患者的自我评估来学习。需要一种远程记录关节状态的机制，包括肿胀和活动范围。为了满足这些需求，Arthronica与国王学院医院风湿科进行了一项横断面诊断准确性研究，以记录关节肿胀的图像并进行测量。然而，该公司在生物力学建模方面缺乏深入的专业知识。Rusli博士在计算建模和肌肉骨骼生物力学方面拥有丰富的经验，他将支持ARSCRONICA研究人员验证RA生物力学模型，以提高远程评估RA疾病活动的技术准备。