EAGER: SCH: AI in Sleep for Rural and Aging Communities

Board Of Regents, Nshe, Obo University Of Nevada, Reno, Reno NV

Investigators

Abstract

Although artificial intelligence (AI) and machine learning have become commonplace, vulnerable populations often do not have input to the design, development, and application of key AI tools that improve the well-being and safety of communities. Reduced variety in the data input of AI tools diminishes the overall power of the technologies and the resulting output. This project uses AI technology to connect patients with sleep research professionals and specialists to obtain sleep health information in a longitudinal fashion. This approach provides support to populations who do not have regular access to sleep specialists, particularly rural and hard to reach populations such as shift workers and aging adults. The project also provides unique patient data to the AI tools to increase the patient population groups involved in optimizing this technology, which will mitigate input bias of the AI tools. This EAGER research effort is a unique opportunity for AI-based technologies for healthcare by having additional variation in population input, and by improving healthcare access to rural and vulnerable populations with a need for sustained access to sleep specialists. Individuals from these groups will be recruited, contributing to the technology’s overall input data producing broadly applicable output results. This project uses a newly synthesized and verified AI technology that uses the frequencies of physiological characteristics during sleep stages as inputs and analyzes resulting outputs for accuracy. The goals of the project are to: 1) optimize the data processing and algorithmic analysis of AI technologies by broadening the input data collected, improving the sensitivity, precision, and applicability of the output from deep learning algorithms for the public, 2) determine the accuracy of the AI technology’s data processing and interpretation and adaptability from unique population groups by comparing collected information with that of participants with similar backgrounds (for example, age, sex, and race) as the rural and other participant groups, and 3) increase access to needed population services through successful adaptability of specialized AI technologies. The project will explore the effectiveness using artificial neural networks in deep learning algorithms to interpret the raw data collected from participants as they sleep. Output produced from the AI tool and scoring of sleep studies (polysomnography) will be comparatively analyzed. The PIs will collaborate with sleep specialists, some who are located more than four hours from the participants’ home or work locations, for baseline polysomnography scoring and raw data analysis. Outputs will be compared with cohort data from similar groups. The outcomes of this project include providing more broad-based input for an AI analysis output function that is more predictable and accurate in analysis. Further, improvement in the health and well-being of the study group and the general population will result from better access to specialist care and use of precision tools for analysis. 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.