I-Corps: Utilizing Machine learning and Artificial Intelligence (AI) for Early Detection and Identification of Mental Disorders

这个I-Corps项目的更广泛影响是开发神经系统疾病的自动诊断工具。 目前，没有单一的定量测试（如血糖测试）可以用来诊断神经系统疾病。 该技术基于先进的深度学习模型，应用于多模态成像，如磁共振成像（MRI）和脑电图（EEG），以进行早期检测。 该技术可用于区分异常脑扫描与健康扫描，并可能有助于诊断神经系统疾病，如自闭症谱系障碍（ASD），注意力缺陷多动障碍（ADHD），阿尔茨海默病（AD），癫痫和帕金森病（PD）。例如，目前的ADHD诊断测试导致每年近一百万儿童的错误诊断，过度诊断或诊断不足。如果成功，所提出的技术可能会提高诊断准确性并改善临床结果。 这个I-Corps项目基于先进机器学习算法的开发，包括深度学习（DL），网络科学原理和数据驱动方法，可以识别神经系统疾病的标志物。目的是提供一种非侵入性的诊断测试，代表神经系统疾病的进展，与神经病学相关，并提供早期检测。对于大多数神经系统疾病，不存在单一的定量测试，或者对于癫痫等疾病，依赖于单一模态数据。 初步数据表明，将DL方法应用于多种形式的大脑数据（功能性MRI - fMRI，结构性MRI - sMRI，EEG）将导致一种基于人工智能（AI）的变革性定量策略来诊断这些疾病-而不受当前临床方法的限制。ASD和ADHD诊断的成功模型已经设计出来，结果表明仅使用MRI数据的准确性提高了28%。癫痫和AD的早期检测技术目前正在开发中。这些模型将作为概念验证和开发解决方案的基础，用于使用多模态成像数据进行多疾病诊断和预测。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this I-Corps project is the development of automated diagnosis tools for neurological disorders. Currently, there is no single quantitative test (like a blood glucose test) that can be done to diagnose neurological disorders. The proposed technology is based on advanced deep learning models applied to multi-modal imaging, such as magnetic resonance imaging (MRI) and electroencephalography (EEG), for early detection. The technology may be used to distinguish abnormal brain scans from healthy scans and may be able to aid in the diagnosis of neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer’s disease (AD), epilepsy, and Parkinson’s disease (PD). For example, current ADHD diagnostic tests result in the mis-, over-, or under-diagnosis of nearly a million children annually. If successful, the proposed technology may lead to enhanced diagnostic accuracy and improved clinical outcomes. This I-Corps project is based on the development of advanced machine learning algorithms including deep learning (DL), network science principles, and data-driven approaches that may identify markers for neurological disorders. The goal is to provide a diagnostic test that is non-invasive, represents progression of the neurological disorder, correlates with symptomatology, and provides early-detection. A single quantitative test does not exist for most neurological disorders or relies on single-modality data for disorders such as epilepsy. Preliminary data suggests that applying DL methods to multiple modalities of brain data (functional MRI - fMRI, structural MRI - sMRI, EEG) will lead to a transformative artificial intelligence (AI)-based, quantitative strategy to diagnose these disorders – without the limitation of the current clinical methods. Successful models for the diagnosis of ASD and ADHD have been designed and results indicate an accuracy improvement of up to 28% using MRI data alone. The technology for early detection of epilepsy and AD are currently under development. These models will serve as proof-of-concept and a base for developing solutions for multi-disorder diagnosis and prediction using multi-modal imaging data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.