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Peripheral Arterial Disease (PAD) is a common form of arterial occlusivedisease that is challenging to evaluate at the point-of-care. Hand-helddopplers are the most ubiquitous device used to evaluate circulation and allowsproviders to audibly "listen" to the blood flow. Providers use the audiblefeedback to subjectively assess whether the sound characteristics areconsistent with Monophasic, Biphasic, or Triphasic waveforms. Subjectiveassessment of doppler sounds raises suspicion of PAD and leads to furthertesting, often delaying definitive treatment. Misdiagnoses are also possiblewith subjective interpretation of doppler waveforms. This paper presents a DeepLearning system that has the ability to predict waveform phasicity throughanalysis of hand-held doppler sounds. We collected 268 four-second recordingson an iPhone taken during a formal vascular lab study in patients withcardiovascular disease. Our end-to-end system works by converting input soundinto a spectrogram which visually represents frequency changes in temporalpatterns. This conversion enables visual differentiation between the phasicityclasses. With these changes present, a custom trained Convolutional NeuralNetwork (CNN) is used for prediction through learned feature extraction. Theperformance of the model was evaluated via calculation of the F1 score andaccuracy metrics. The system received an F1 score of 90.57% and an accuracy of96.23%. Our Deep Learning system is not computationally expensive and has theability for integration within several applications. When used in a clinic,this system has the capability of preventing misdiagnosis and givespractitioners a second opinion that can be useful in the evaluation of PAD.

Waveform Phasicity Prediction from Arterial Sounds through Spectrogram Analysis using Convolutional Neural Networks for Limb Perfusion Assessment