SCH: Bringing Intelligence to Pulmonology: New AI-Enabled Systems for Pulmonary Function Tests Anytime and Anywhere

肺部疾病是公共卫生面临的一大挑战，而肺功能测试是评价人体呼吸道力学变化的主要方法，而呼吸道力学是肺部疾病的关键症状。由于肺部疾病频繁加重的可能性，使患者能够在临床外随时随地获得PFT是很重要的，但目前大多数门诊PFT技术过于繁琐和昂贵，无法在临床上使用。该项目通过在商用智能手机上开发新的集成人工智能(AI)和传感系统，随时随地实现PFT，以应对这些挑战。由于肺部疾病广泛影响人类社会，并导致每年数十亿医疗保健相关成本，该项目通过实现高效的疾病监测、诊断和临床管理，具有造福社会的巨大潜力。该项目还通过制作有助于了解疾病机制的数据集，以及开发新的课程，传播教育和培训研究，并让未被充分代表的学生参与研究，为社会做出贡献。该项目的主要目标是使用商品智能手机实现高精度、适应性和通用性的PFT。该项目包括四项研究任务：(1)在商用智能手机上设计新的声学传感系统，测量人类呼吸道管腔的尺寸和特征；(2)从声学感官数据中提取适当的生物标记物特征，用于疾病评估；(3)开发可应用于评估不同肺部疾病的通用机器学习(ML)模型；(4)探索使用新的联合学习技术训练ML模型的分布式和异步方法。该奖项反映了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。

Pulmonary diseases constitute a major public health challenge, and pulmonary function testing (PFT) is the main method of evaluating the changes in human airway mechanics, which are the key symptoms of pulmonary diseases. Due to the possibility of frequent exacerbations in pulmonary diseases, it is important that PFT is accessible to patients anytime and anywhere out of clinic, but most of current in-clinic PFT techniques are too cumbersome and expensive to be used out of clinic. This project addresses these challenges to enable PFT anytime and anywhere, by developing new and integrated artificial intelligence (AI) and sensing systems on commodity smartphones. As pulmonary diseases widely affect the human society and result in billions of annual healthcare related costs, this project has great potential to benefit society by enabling efficient disease monitoring, diagnosis and management out of clinic. This project is also contributing to society by producing datasets that help understand the disease mechanisms, as well as developing new curricula, disseminating research for education and training, and engaging underrepresented students in research.The primary goal of this project is to enable highly accurate, adaptable and generic PFT out of clinic using commodity smartphones. The project consists of four research tasks: (1) designing new acoustic sensing systems on commodity smartphones that measure the humans’ airway lumen dimensions and characteristics; (2) extracting appropriate biomarker profiles from acoustic sensory data for disease evaluation; (3) developing generalizable machine learning (ML) models that can be applied to evaluating different pulmonary diseases; (4) exploring distributed and asynchronous methods of training the ML models with new federated learning techniques.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.