I-Corps: diagnostic & patient management tool for physicians treating movement disorders

这个i-Corps项目的更广泛的影响/商业潜力是加速神经精神化合物的药物开发进程，并改善脑部疾病的临床管理。该项目将评估基于深度学习的姿势跟踪算法的进步是否可以在生物技术/制药和医疗保健领域带来新的商业机会。行为是大脑过程复杂协调的结果，并受到疾病或损伤的影响。患者或动物的行为谱是关于其神经精神健康的丰富的非侵入性数据来源，但挖掘这类数据的工具有限。解决行为量化问题有可能提高神经精神科药物的开发效率，因为它可以让生物技术/制药公司更好地了解他们的药物从第一次在动物身上测试到关键的人体研究一直是如何起作用的。行为量化工具还可以帮助临床医生治疗神经精神疾病，由于其复杂性，通常需要广泛的培训才能正确诊断和处理。这个i-Corps项目是基于一种基于深度学习的行为量化工具的开发，用于药物开发和临床医学。目前衡量神经精神病学干预效果的方法受到其敏感性、有效性和重复性的限制。神经科学的突破为药物开发人员阐明了新的靶向机制，但评估新治疗方法的疗效的技术有限。此外，神经精神测试的动物模型对临床结果的可译性有限，这使得药物开发商很难确定资产的优先顺序。基于深度学习的行为测量工具通过自动进行高灵敏度和高特异度的行为分析，为这一问题提供了一种非侵入性的解决方案。这些工具可以提取药物开发人员目前无法获得的见解，这不仅将促进对疾病生物学的理解，还将收集用于药物批准的关键数据。生物技术/制药公司在所有开发阶段都能有力地确定治疗效果，这降低了目前与神经精神药物开发相关的监管和商业风险。同样，医生将能够使用先进的行为量化技术将患者的症状与“数字指纹”库进行比较，以准确诊断病情和管理症状。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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/commercial potential of this I-Corps project is to accelerate the drug development process for neuropsychiatric compounds and improve the clinical management of brain disorders. This project will evaluate whether advances in deep learning-based pose tracking algorithms can lead to new business opportunities within both biotech/pharma and healthcare. Behavior is a result of the complex coordination of brain processes and is affected by disease or impairment. The behavioral repertoire of a patient or animal is a rich noninvasive source of data about its neuropsychiatric health, but limited tools exist to mine this type of data. Solving the problem of behavioral quantification has the potential to make neuropsychiatric drug development more efficient by providing biotech/pharma with a better understanding of how their drugs are working from the time they are first tested in animals all the way through pivotal human studies. Behavioral quantification tools can also assist clinicians with the treatment of neuropsychiatric diseases, which due to their complexity typically require extensive training to properly diagnose and manage. This I-Corps project is based on the development of a deep learning based behavioral quantification tool for use in drug development and clinical medicine. Current approaches to measure the efficacy of neuropsychiatric interventions are limited by their sensitivity, efficiency, and reproducibility. Breakthroughs in neuroscience have elucidated new mechanisms for drug developers to target, but limited techniques exist to evaluate the efficacy of new treatments. In addition, animal models for neuropsychiatric testing have limited translatability to clinical results, making asset prioritization difficult for drug developers. Deep learning based behavioral measurement tools serve as a noninvasive solution to this problem by automating behavioral analysis with high sensitivity and specificity. These tools can extract insights that are currently inaccessible to drug developers, which will not only lead to advances in the understanding of the biology of disease, but also the collection of pivotal data for drug approval. The ability of biotech/pharma to robustly characterize therapeutic efficacy across all stages of development reduces the regulatory and commercial risks currently associated with neuropsychiatric drug development. Similarly, physicians would be able to use advanced behavioral quantification techniques to compare a patient’s symptoms against a library of “digital fingerprints” to accurately diagnose conditions and manage symptoms.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.