Using artificial intelligence to mitigate scanner bias in brain disorders

使用人工智能减轻大脑疾病的扫描仪偏差

• 批准号: MR/X005445/1
• 负责人: Rafael Dias
• 金额: $ 13.9万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX005445%2F1
• 关键词: Using; artificial; intelligence; mitigate; scanner

Brain-based disorders represent 14% of the global burden of disease. Artificial Intelligence (AI) applications to neuroimaging have the potential to improve the detection and treatment of these disorders. This potential has been demonstrated in controlled research setups, but the real-world clinical implementation of these techniques is limited by differences in scanning equipment and protocols. These differences, known as scanner bias, means that diagnostic and prognostic AI applications developed using images from a certain machine tend to perform poorly when applied to images from different machines. There is preliminary evidence that some AI-based approaches can mitigate this problem, however, they are limited to healthy subjects. We will adapt and validate Neuroharmony, an AI-based solution for this problem, using data from clinical populations with psychiatric or neurological disorders obtained across multiple machines. The results obtained here will improve Neuroharmony, validate it for clinical application and make it available to the research and clinical communities. This tool can help bridge the gap between AI-based research and clinical practice.

以大脑为基础的疾病占全球疾病负担的14%。人工智能(AI)在神经成像方面的应用有可能改善这些疾病的检测和治疗。这种潜力已经在对照研究中得到了证明，但这些技术在现实世界中的临床实施受到扫描设备和方案的差异的限制。这些差异被称为扫描仪偏差，这意味着使用来自某台机器的图像开发的诊断和预测人工智能应用程序在应用于来自不同机器的图像时往往表现不佳。有初步证据表明，一些基于人工智能的方法可以缓解这一问题，然而，它们仅限于健康受试者。我们将使用从多台机器获得的患有精神或神经障碍的临床人群的数据，调整和验证NeuroHarmonity，这是一个针对这个问题的基于人工智能的解决方案。这里获得的结果将改善神经和谐，验证其临床应用，并使其可用于研究和临床社区。这一工具可以帮助弥合基于人工智能的研究和临床实践之间的差距。