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

SCH：为肺病学带来智能：新型人工智能系统可随时随地进行肺功能测试

• 批准号: 2205360
• 负责人: Wei Gao
• 金额: $ 110万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2205360&HistoricalAwards=false
• 关键词: SCH; Bringing; Intelligence; Pulmonology; New

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.

肺部疾病是一个重大的公共卫生挑战，肺功能检测（Pulmonary Function Testing，PFT）是评价肺部疾病的主要症状--气道力学变化的主要方法。由于肺部疾病频繁加重的可能性，重要的是PFT可供患者在诊所外随时随地使用，但目前大多数诊所内PFT技术过于繁琐和昂贵，无法在诊所外使用。该项目通过在商品智能手机上开发新的集成人工智能（AI）和传感系统来应对这些挑战，以便随时随地实现PFT。由于肺部疾病广泛影响人类社会，每年造成数十亿美元的医疗相关费用，该项目具有巨大的潜力，可以通过实现有效的疾病监测，诊断和临床管理来造福社会。该项目还通过制作有助于了解疾病机制的数据集，开发新课程，传播教育和培训研究，并让代表性不足的学生参与研究，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度准确，适应性强和通用的PFT。该项目包括四项研究任务：（1）在商品智能手机上设计新的声学传感系统，测量人类气道管腔的尺寸和特征;（2）从声学传感数据中提取适当的生物标志物谱用于疾病评估;（3）开发可应用于评估不同肺部疾病的可推广机器学习（ML）模型;（4）开发新的机器学习模型。（4）探索使用新的联邦学习技术训练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知识价值和更广泛的影响审查标准进行评估而被认为值得支持。

项目成果

Out-Clinic Pulmonary Disease Evaluation via Acoustic Sensing and Multi-Task Learning on Commodity Smartphones

通过商品智能手机上的声学传感和多任务学习进行临床外肺部疾病评估

• DOI: 10.1145/3560905.3568437
• 发表时间: 2022
• 期刊: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
• 作者: Yin, Xiangyu;Huang, Kai;Forno, Erick;Chen, Wei;Huang, Heng;Gao, Wei
• 通讯作者: Gao, Wei

AiFi: AI-Enabled WiFi Interference Cancellation with Commodity PHY-Layer Information

• DOI: 10.1145/3560905.3568537
• 发表时间: 2022-11
• 期刊: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
• 作者: Ruirong Chen;Kai Huang;Wei Gao

Real-time neural network inference on extremely weak devices: agile offloading with explainable AI

• DOI: 10.1145/3495243.3560551
• 发表时间: 2022-10
• 期刊: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking
• 作者: Kai Huang;Wei Gao

PTEase: Objective Airway Examination for Pulmonary Telemedicine using Commodity Smartphones

PTEase：使用商用智能手机进行肺部远程医疗的客观气道检查

• DOI: 10.1145/3581791.3596854
• 发表时间: 2023
• 期刊: Applications and Services
• 作者: Yin, Xiangyu;Huang, Kai;Forno, Erick;Chen, Wei;Huang, Heng;Gao, Wei
• 通讯作者: Gao, Wei