I-Corps: Automated sound analysis tool for early detection of arteriovenous fistula stenosis/failure in hemodialysis patients

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The broader impact/commercial potential of this I-Corps project is the development of biomedical acoustic analysis software. Sounds produced by the body often reflect underlying pathophysiological processes and are easy to collect but difficult to interpret. The proposed concept is to revolutionize auscultation, an important element of the physical exam, and transform it from a qualitative art to a quantitative science. Potential applications include the analysis of heart sounds, blood flow sounds, lung sounds, cough sounds, bowel sounds, nerve conduction sound signals, and muscle cell contraction sound signals. Early detection of potential problems from biomedical acoustic analysis may initiate the necessary preventative actions, mitigating downstream costs and improving patient quality of life. The I-Corps project is based on the development of an artificial intelligence (AI) system that is designed to detect signs of early stenosis of arteriovenous (AV) fistulas in hemodialysis patients using biomedical acoustic analysis. At points of abnormal blood vessel narrowing, turbulent flow produces distinct frequencies, vibrations, and sound characteristics from laminar flow. The proposed AI software system comprises of a machine learning model that may be used to classify fistulas as either patent or stenotic based on the acoustic signals and their relative variations along the vascular access. The machine learning model is trained on AV fistula recordings collected using a digital stethoscope and flow statuses validated by duplex ultrasound. In previous studies, the machine learning model achieved an accuracy of 80% in classifying AV fistula patency.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.