Interpretable Deep Biomarker for Serial Monitoring of Carotid Atherosclerosis Based on Three-Dimensional Ultrasound Imaging

Xueli Chen, Xinqi Fan, Bernard Chiu

Abstract: We developed an interpretable deep biomarker known as Siamese change biomarker generation network (SCBG-Net) to evaluate the effects of therapies on carotid atherosclerosis based on the vessel wall and plaque volume and texture features extracted from three-dimensional ultrasound (3DUS) images. To the best of our knowledge, SCBG-Net is the first deep network developed for serial monitoring of carotid plaque changes. SCBG-Net automatically integrates volume and textural features extracted from 3DUS to generate a change biomarker called AutoVT (standing for Automatic integration of Volume and Textural features) that is sensitive to dietary treatments. The proposed AutoVT improves the cost-effectiveness of clinical trials required to establish the benefit of novel treatments, thereby decreasing the period that new anti-atherosclerotic treatments are withheld from patients needing them. To facilitate the interpretation of AutoVT, we developed an algorithm to generate change biomarker activation maps (CBAM) localizing regions having an important effect on AutoVT. The ability to visualize locations with prominent plaque progression/regression afforded by CBAM improves the interpretability of the proposed deep biomarker. Improvement in interpretability would allow the deep biomarker to gain sufficient trust from clinicians for them to incorporate the model into clinical workflow.