Neural network-based arterial diameter estimation from ultrasound data.

Cardiovascular diseases are the leading cause of mortality and early assessment of carotid artery abnormalities with ultrasound is key for effective prevention. Obtaining the carotid diameter waveform is essential for hemodynamic parameter extraction. However, since it is not a trivial task to automate, compact computational models are needed to operate reliably in view of physiological variability. Modern machine learning (ML) techniques hold promise for fully automated carotid diameter extraction from ultrasonic data without requiring annotation by trained clinicians. Using a conventional digital signal processing (DSP) based approach as reference, our goal is to (a) build data-driven ML models to identify and track the carotid diameter, and (b) keep the computational complexity minimal for deployment in embedded systems. A ML pipeline is developed to estimate the carotid artery diameter from Hilbert-transformed ultrasound signals acquired at 500Hz sampling frequency. The proposed ML pipeline consists of 3 processing stages: two neural-network (NN) models and a smoothing filter. The first NN, a compact 3-layer convolutional NN (CNN), is a region-of-interest (ROI) detector confining the tracking to a reduced portion of the ultrasound signal. The second NN, an 8-layer (5 convolutional, 3 fully-connected) CNN, tracks the arterial diameter. It is followed by a smoothing filter for removing any superimposed artifacts. Data was acquired from 6 subjects (4 male, 2 female, 37 ± 7 years, baseline mean arterial pressure 86.3 ± 7.6 mmHg) at rest and with diameter variation induced by paced breathing and a hand grip intervention. The label reference is extracted from a fine-tuned DSP-based approach. After training, diameter waveforms are extracted and compared to the DSP reference. The predicted diameter waveform from the proposed NN-based pipeline has near perfect temporal alignment with the reference signal and does not suffer from drift. Specifically, we obtain a Pearson correlation coefficient of r = 0.87 between prediction and reference waveforms. The mean absolute deviation of the arterial diameter prediction was quantified as 0.077 mm, corresponding to a 1% error given an average carotid artery diameter of 7.5 mm in the study population. This work proposed and evaluated an ML neural network-based pipeline to track the carotid artery diameter from an ultrasound stream of A-mode frames. By contrast to current clinical practice, the proposed solution does not rely on specialist intervention (e.g. imaging markers) to track the arterial diameter. In contrast to conventional DSP-based counterpart solutions, the ML-based approach does not require handcrafted heuristics and manual fine-tuning to produce reliable estimates. Being trainable from small cohort data and reasonably fast, it is useful for quick deployment and easy to adjust accounting for demographic variability. Finally, its reliance on A-mode ultrasound frames renders the solution promising for miniaturization and deployment in on-line clinical and ambulatory monitoring.