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

Autori
Saba, Luca ; Biswas, Mainak ; Suri, Harman S. ; Višković, Klaudija ; Laird, John R. ; Cuadrado- Godia, Elisa ; Nicolaides, Andrew ; Khanna, N. N. ; Viswanathan, Vijay ; Suri, Jasjit S.

Izvornik
Cardiovascular Diagnosis and Therapy (2223-3652) 9 (2019), 5; 439-461

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Atherosclerosis ; carotid stenosis severity index (carotid SSI) ; reliable ; accurate ; deep learning (DL) ; performance

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti

POVEZANOST RADA