Vote for Emory AI/brain disease research in ARPA-H Dash online competition

By Chris Cox April 21, 2023

Emory is participating in ARPA-H Dash, a March Madness-style bracket competition that features innovative ideas in head-to-head competition, rather than basketball teams. Ideas were selected based on contributions from potential participants. The competition bracket opened on April 17 and first-round voting closes Sunday, April 23, at midnight ET.

Emory’s contribution is an idea from Reza Sameni, associate professor of biomedical engineering in the School of Medicine, to develop an in-ear AI technology that can lead to earlier detection of brain diseases such as dementia and Alzheimer’s.

Please visit the ARPA-H Dash competition on the Polyplexus platform to vote for Emory (registration required): ARPA-H Dash.

Voting is open to the public, but you don’t have to fill out the entire bracket — you can just vote for Emory’s contribution. The opening and closing dates for voting on each round are:

Round 1 (of 64): opened April 17 and closes April 24
Round 2 (of 32): opens April 24 and closes April 30
Round 3 (of 16): opens May 1 and closes May 4
Round 4 (of 8): opens May 5 and closes May 8
Round 5 (of 4): opens May 9 and closes May 10
Finals: Opens May 10 and closes May 11

To help Emory win, please get friends and followers involved on social media by using the hashtag #EmoryDash.

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Title: Passive-Brain-Audition

Proposed health transformation: To develop an artificial intelligence in-ear technology for early and proactive detection of brain aging and dementia from brain wave responses to environmental and memorable sounds during daily life.

Current implementation: Brain aging and dementia are currently assessed by regular in-clinic studies. The procedure is timely, costly and often too late, after a patient's progression to severe conditions such as Alzheimer's.

What's new: An outside-clinic in-ear device with embedded AI technology records environmental sounds, plays memorable audio and analyzes patient's brain wave responses to identify onsets of aging and dementia.

Evidence statement: The brain responds to auditory stimuli and machine learning models can use the shape and speed of brain wave responses to identify mental health and detect early onsets of brain aging and dementia.

Evidence citation: Wireless and portable daily soundscape monitoring system for auditory profiling: diagnostic tool for Alzheimer's disease. IEEE EMBC 2020

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