Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification

Skandha S Sanagala; Andrew Nicolaides; Suneet K Gupta; Vijaya K Koppula; Luca Saba; Sushant Agarwal; Amer M Johri; Manudeep S Kalra; Jasjit S Suri

doi:10.3390/diagnostics11112109

Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification

Diagnostics (Basel). 2021 Nov 15;11(11):2109. doi: 10.3390/diagnostics11112109.

Authors

Skandha S Sanagala^{1

2}, Andrew Nicolaides³, Suneet K Gupta², Vijaya K Koppula¹, Luca Saba⁴, Sushant Agarwal⁵, Amer M Johri⁶, Manudeep S Kalra⁷, Jasjit S Suri⁸

Affiliations

¹ CSE Department, CMR College of Engineering & Technology, Hyderabad 501401, TS, India.
² CSE Department, Bennett University, Greater Noida 203206, UP, India.
³ Vascular Screening and Diagnostic Centre, University of Nicosia, Nicosia 1700, Cyprus.
⁴ Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy.
⁵ Global Biomedical Technologies, Roseville, CA 95661, USA.
⁶ Division of Cardiology, Queen's University, Kingston, ON K7L 3N6, Canada.
⁷ Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
⁸ Stroke Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA 95661, USA.

Abstract

Background and Purpose: Only 1-2% of the internal carotid artery asymptomatic plaques are unstable as a result of >80% stenosis. Thus, unnecessary efforts can be saved if these plaques can be characterized and classified into symptomatic and asymptomatic using non-invasive B-mode ultrasound. Earlier plaque tissue characterization (PTC) methods were machine learning (ML)-based, which used hand-crafted features that yielded lower accuracy and unreliability. The proposed study shows the role of transfer learning (TL)-based deep learning models for PTC. Methods: As pertained weights were used in the supercomputer framework, we hypothesize that transfer learning (TL) provides improved performance compared with deep learning. We applied 11 kinds of artificial intelligence (AI) models, 10 of them were augmented and optimized using TL approaches-a class of Atheromatic™ 2.0 _TL (AtheroPoint™, Roseville, CA, USA) that consisted of (i-ii) Visual Geometric Group-16, 19 (VGG16, 19); (iii) Inception V3 (IV3); (iv-v) DenseNet121, 169; (vi) XceptionNet; (vii) ResNet50; (viii) MobileNet; (ix) AlexNet; (x) SqueezeNet; and one DL-based (xi) SuriNet-derived from UNet. We benchmark 11 AI models against our earlier deep convolutional neural network (DCNN) model. Results: The best performing TL was MobileNet, with accuracy and area-under-the-curve (AUC) pairs of 96.10 ± 3% and 0.961 (p < 0.0001), respectively. In DL, DCNN was comparable to SuriNet, with an accuracy of 95.66% and 92.7 ± 5.66%, and an AUC of 0.956 (p < 0.0001) and 0.927 (p < 0.0001), respectively. We validated the performance of the AI architectures with established biomarkers such as greyscale median (GSM), fractal dimension (FD), higher-order spectra (HOS), and visual heatmaps. We benchmarked against previously developed Atheromatic™ 1.0 _ML and showed an improvement of 12.9%. Conclusions: TL is a powerful AI tool for PTC into symptomatic and asymptomatic plaques.

Keywords: artificial intelligence; carotid plaque characterization; heatmaps; stroke; symptomatic vs. asymptomatic; transfer learning.