Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: a deep learning paradigm
Luca Saba1, Mainak Biswas2, Harman S Suri3
1Department of Radiology, A.O.U., Cagliari, Italy.
Background:
Stroke is in the top three leading causes of death worldwide. Non-invasive monitoring of stroke can be accomplished via stenosis measurements. The current conventional image-based methods for these measurements are not accurate and reliable. They do not incorporate shape and intelligent learning component in their design.
Methods:
In this study, we propose a deep learning (DL)-based methodology for accurate measurement of stenosis in common carotid artery (CCA) ultrasound (US) scans using a class of AtheroEdge system from AtheroPoint, USA. Three radiologists manually traced the lumen-intima (LI) for the near and the far walls, respectively, which served as a gold standard (GS) for training the DL-based model. Three DL-based systems were developed based on three types of GS.
Results:
IRB approved (Toho University, Japan) 407 US scans from 204 patients were collected. The risk was characterized into three classes: low, moderate, and high-risk. The area-under-curve (AUC) corresponding to three DL systems using receiver operating characteristic (ROC) analysis computed were: 0.90, 0.94 and 0.86, respectively.
Conclusions:
Novel DL-based strategy showed reliable, accurate and stable stenosis severity index (SSI) measurements.
