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Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: a deep learning paradigm

  
@article{CDT29523,
	author = {Luca Saba and Mainak Biswas and Harman S. Suri and Klaudija Viskovic and John R. Laird and Elisa Cuadrado-Godia and Andrew Nicolaides and N. N. Khanna and Vijay Viswanathan and Jasjit S. Suri},
	title = {Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: a deep learning paradigm},
	journal = {Cardiovascular Diagnosis and Therapy},
	volume = {9},
	number = {5},
	year = {2019},
	keywords = {},
	abstract = {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.},
	issn = {2223-3660},	url = {http://cdt.amegroups.com/article/view/29523}
}