Abstract
The most ubiquitous tool utilized at the point-of-care within clinic for the evaluation of Peripheral Arterial Disease (PAD) or limb perfusion is the hand-held doppler. Providers interpret audible circulatory sounds in order to predict waveform phasicity as Monophasic (severe disease), Biphasic (moderate disease), or Triphasic (normal). This prediction is highly subjective. With any doubt in interpretation, patients are referred to an ultrasound laboratory for formal evaluation which can delay definitive diagnosis and treatment. This paper proposes a novel deep learning system integrated within a hardware doppler platform for low-cost, fast, and accessible assessment of disease at the point-of-care. The system converts input sound into a spectrogram and classifies waveform phasicity with a neural network through learned visual temporal amplitude patterns. The hardware system consists of a low-cost single board computer and simple peripherals including a microphone and LCD display. The final system received a validation accuracy of 96.23%, an F1 score of 90.57%, and was additionally tested within a manually simulated clinical environment with interference in which it received an accuracy of 91.66%. Such a tool can be used to more objectively and efficiently diagnose PAD at the point-of-care and prevent delays in intervention.
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Rao, A., Chaudhari, A., Aalami, O. (2021). Development of the Next Generation Hand-Held Doppler with Waveform Phasicity Predictive Capabilities Using Deep Learning. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning. DCL PPML LL-COVID19 CLIP 2021 2021 2021 2021. Lecture Notes in Computer Science(), vol 12969. Springer, Cham. https://doi.org/10.1007/978-3-030-90874-4_6
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