Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never before

Bridge2AI：声音作为健康的生物标志物 - 建立一个符合道德规范的生物声学数据库，以前所未有的方式了解疾病

• 批准号: 10858564
• 负责人: Yael Emilie Bensoussan
• 金额: $ 530.2万
• 依托单位: UNIVERSITY OF SOUTH FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10858564
• 关键词: Acoustics; Address; Adoption; Alzheimer' s Disease; Amplifiers; Apple; Attention; Benign; Biological Markers; Bipolar Disorder; Bridge to Artificial Intelligence; Categories; Childhood; Chronic Obstructive Pulmonary Disease; Clinical; Cloud Computing; Collaborations; Communities; Competence; Computer software; Consent; Data; Data Analyses; Data Collection; Data Protection; Databases; Development; Diagnosis; Disease; Educational Curriculum; Electronic Health Record; Engineering; Ensure; Ethics; FAIR principles; Fostering; Friends; Future Generations; Generations; Genomics; Guidelines; Health; Heart failure; Human; Infrastructure; Institution; Larynx; Lesion; Link; Literature; Malignant neoplasm of larynx; Medical; Mental Depression; Mental disorders; Mentorship; Modeling; Mood Disorders; Neurodegenerative Disorders; Outcome; Paralysed; Parkinson Disease; Pathology; Patients; Pneumonia; Population; Research; Research Personnel; Respiration Disorders; Schizophrenia; Scholarship; Source; Speech Delay; Standardization; Stroke; Technology; Validation; Voice; Voice Quality; Workforce Development; artificial intelligence algorithm; autism spectrum disorder; clinical application; clinical care; data acquisition; data preservation; data privacy; data sharing; federated learning; innovation; insight; multidisciplinary; multimodality; nervous system disorder; patient privacy; predictive modeling; privacy protection; radiomics; research and development; screening; skill acquisition; smartphone application; software infrastructure; tool; tool development; trustworthiness; underserved community; user-friendly; vocal cord; Acoustics; Address; Adoption; Alzheimer' s Disease; Amplifiers; Apple; Attention; Benign; Biological Markers; Bipolar Disorder; Bridge to Artificial Intelligence; Categories; Childhood; Chronic Obstructive Pulmonary Disease; Clinical; Cloud Computing; Collaborations; Communities; Competence; Computer software; Consent; Data; Data Analyses; Data Collection; Data Protection; Databases; Development; Diagnosis; Disease; Educational Curriculum; Electronic Health Record; Engineering; Ensure; Ethics; FAIR principles; Fostering; Friends; Future Generations; Generations; Genomics; Guidelines; Health; Heart failure; Human; Infrastructure; Institution; Larynx; Lesion; Link; Literature; Malignant neoplasm of larynx; Medical; Mental Depression; Mental disorders; Mentorship; Modeling; Mood Disorders; Neurodegenerative Disorders; Outcome; Paralysed; Parkinson Disease; Pathology; Patients; Pneumonia; Population; Research; Research Personnel; Respiration Disorders; Schizophrenia; Scholarship; Source; Speech Delay; Standardization; Stroke; Technology; Validation; Voice; Voice Quality; Workforce Development; artificial intelligence algorithm; autism spectrum disorder; clinical application; clinical care; data acquisition; data preservation; data privacy; data sharing; federated learning; innovation; insight; multidisciplinary; multimodality; nervous system disorder; patient privacy; predictive modeling; privacy protection; radiomics; research and development; screening; skill acquisition; smartphone application; software infrastructure; tool; tool development; trustworthiness; underserved community; user-friendly; vocal cord

Our group aims to integrate the use of voice as biomarker of health in clinical care by generating a substantial multi-institutional, ethically sourced, and diverse voice database linked to multimodal health biomarkers to fuel voice AI research and build predictive models to assist in screening, diagnosis, and treatment of a broad range of diseases. Data collection will be made possible by software through a smartphone application linked to electronic health records (EHR) and other health biomarkers such as radiomics, and genomics, and supported by federated learning technology to protect data privacy. Based on the existing literature and ongoing research in different fields of voice research, our group has identified 5 disease categories for which voice changes have been associated to specific diseases and around which we aim to center the data acquisition efforts: 1. Vocal Pathologies (Laryngeal cancers, Vocal fold paralysis, Benign laryngeal lesions) 2. Neurological and Neurodegenerative Disorders (Alzheimer’s, Parkinson’s, Stroke, ALS) 3. Mood and Psychiatric Disorders (Depression, Schizophrenia, Bipolar Disorders) 4. Respiratory disorders (Pneumonia, COPD, Heart Failure, OSA) 5. Pediatric diseases (Autism, Speech Delay) Specific Aim #1: Data Acquisition Module: - To build a multi-modal, multi-institutional, large scale, diverse and ethically sourced human voice database linked to other biomarkers of health that is AI/ML friendly to fuel voice AI research Specific Aim #2: Standard Module: - To introduce the field of acoustic biomarkers by developing new standards of acoustic and voice data collection and analysis for voice AI research. Specific Aim #3: Tool Development and optimization - To develop a software and cloud infrastructure for automated voice data collection through a smartphone application that allows non-invasive, user-friendly, high quality voice data collection while minimizing human manipulation. This will include integrated acoustic amplifiers and acoustic quality standardization. - To implement Federated Learning technology to allow analysis of multi-institutional data while minimizing data sharing and preserving patient privacy Specific Aim #4: Ethics Module - To integrate existing scholarship, tools, and guidance with development of new standard and normative insights for identifying, anticipating, addressing, and providing guidance on ethical and trustworthy issues from voice data generation and AI/ML research and development to clinical adoption and downstream health decisions and outcomes. - To develop new guidelines for consenting to voice data collection, voice data sharing and utilization in the context of voice AI technology Specific Aim # 5: Teaming Module: - To build bridges between the medical voice research world, the acoustic engineers, and the AI/ML world to promote the integration of tangible clinical application for Voice AI algorithms Specific Aim #6: Skills and Workforce Development Module - To develop a unique curriculum on voice biomarkers of health and the development, validation, and implementation for AI models that are FAIR and CARE - To create a community of voice AI researchers, especially those from underserved communities, and foster collaborations to promote application of ML for Voice Research - To engage a broad range of learners with competency assessment and mentorship

我们的小组旨在通过产生与多模式健康生物标志物相关的大量多机构，道德来源和潜水的语音数据库来融合临床护理中健康生物标志物的使用，以促进语音AI研究并建立预测模型，以帮助筛查，诊断和疾病范围广泛范围。软件将通过与电子健康记录（EHR）和其他健康生物标志物（例如放射线组织和基因组学）相关的智能手机应用程序来收集数据，并得到联合学习技术的支持，以保护数据隐私。 基于现有的文献和语音研究领域的持续研究，我们的小组确定了5种疾病类别，这些疾病已与特定疾病相关，我们旨在将数据获取工作集中为中心： 1。声病理学（喉癌，声折叠瘫痪，良性喉病变） 2。神经和神经退行性疾病（阿尔茨海默氏症，帕金森氏症，中风，ALS） 3。情绪和精神疾病（抑郁症，精神分裂症，躁郁症） 4。呼吸系统疾病（肺炎，COPD，心力衰竭，OSA） 5。小儿疾病（自闭症，语音延迟） 特定目标＃1：数据采集模块： - 建立一个多模式，多机构，大规模的，潜水员和道德采购的人音数据库，该数据库与其他健康的生物标志物相关联，该数据库对AI/ML友好友好，以助长语音AI Research 特定目标＃2：标准模块： - 通过为语音AI研究制定声学和语音数据收集和分析的新标准来介绍声学生物标志物的领域。 特定目标＃3：工具开发和优化 - 通过智能手机应用程序为自动语音数据收集开发软件和云基础架构，该应用程序允许无创，用户友好，高质量的语音数据收集，同时最大程度地减少人类的操作。这将包括集成的声学放大器和声学质量标准化。 - 实施联合学习技术，以允许分析多机构数据，同时最大程度地减少数据共享并保留患者隐私 特定目标＃4：道德模块 - 将现有的科学，工具和指导与开发新的标准和正常见解的开发，以识别，预期，解决并提供有关道德和可信赖的问题的指导，从语音数据生成以及AI/ML研发以及临床采用以及下游健康决策和下游的健康决策和成果。 - 制定同意语音数据收集的新准则，语音数据共享和使用语音AI技术 特定目标＃5：组合模块： - 在医学语音研究世界，声学工程师和AI/ML世界之间建造桥梁，以促进语音AI算法的有形临床应用的整合 特定目标＃6：技能和劳动力发展模块 - 开发有关健康模型的健康和开发，验证和实施的语音生物标志物的独特课程 - 建立一个语音AI研究人员，尤其是服务不足的社区的社区，并促进合作促进ML在语音研究中的应用 - 与能力评估和心态有关的广泛学习者