Using artificial intelligence to mitigate scanner bias in brain disorders

脑部疾病占全球疾病负担的14%。人工智能（AI）在神经成像中的应用有可能改善这些疾病的检测和治疗。这种潜力已在对照研究中得到证实，但由于扫描设备和方案的差异，这些技术在现实世界的临床应用受到限制。这些差异被称为扫描仪偏差，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。我们将采用并验证Neuroharmony，这是一种基于人工智能的解决方案，使用从多台机器上获得的精神或神经疾病临床人群的数据。这里获得的结果将改进Neuroharmony，验证其临床应用，并使其可用于研究和临床社区。这个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。

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.