Cardiovascular Risk Assessment using Arterial Stiffness: A Deep Learning Framework

Background: Atherosclerotic cardiovascular disease (CVD) is severe and early-stage detection is crucial. Elevated arterial stiffness observed in childhood atherosclerosis is associated with CVD. Stiffness is an efficient marker of CVD in hypertensives. Assessment of stiffness includes waveform analysis and image-based techniques. Researchers observed several challenges: real-time application, accuracy, operator variability, image quality, scanning procedure, instrument variability and deficiency of standardized procedure in the assessment. Methods: We searched PubMed, Embase and Cochrane online library from inception up to July 2020. Multiple articles on stiffness, pulse wave velocity, assessment and deep learning (DL)-based methods were analysed. Above all, a DL-based technique for assessment of stiffness from cine-loop is proposed. The method includes region of interest (ROI) localisation in multiple frames, segmentation of lumen and parameter estimation. Results: Compared to conventional methods DL provide improved result in lumen diameter and intima-media thickness (IMT) measurements. Using convolutional neural network (CNN), IMT error was 0.08 mm. Further, error using extreme learning machine-autoencoder was 5.79±34.42 . Furthermore, Jaccard index and Dice similarity in fully convolution neural network (FCN) manifested 0.94 and 0.97 for lumen segmentation respectively. Conclusion: This paper focuses on the association of stiffness and atherosclerosis leading to CVD. Success of image-based stiffness estimation depends on the visibility and orientation of arteries, operator experience, intensity variation, shadowing, artefacts, and noise. Traditional methods include transformations to compensate for these challenges. The success of DL-based techniques in segmentation and localisation inspired application in stiffness measurement. DL is used to estimate stiffness from cine-loop.