Literature DB >> 27318571

Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial.

Luca Saba1, Sumit K Banchhor2, Harman S Suri3, Narendra D Londhe2, Tadashi Araki4, Nobutaka Ikeda5, Klaudija Viskovic6, Shoaib Shafique7, John R Laird8, Ajay Gupta9, Andrew Nicolaides10, Jasjit S Suri11.   

Abstract

This study presents AtheroCloud™ - a novel cloud-based smart carotid intima-media thickness (cIMT) measurement tool using B-mode ultrasound for stroke/cardiovascular risk assessment and its stratification. This is an anytime-anywhere clinical tool for routine screening and multi-center clinical trials. In this pilot study, the physician can upload ultrasound scans in one of the following formats (DICOM, JPEG, BMP, PNG, GIF or TIFF) directly into the proprietary cloud of AtheroPoint from the local server of the physician's office. They can then run the intelligent and automated AtheroCloud™ cIMT measurements in point-of-care settings in less than five seconds per image, while saving the vascular reports in the cloud. We statistically benchmark AtheroCloud™ cIMT readings against sonographer (a registered vascular technologist) readings and manual measurements derived from the tracings of the radiologist. One hundred patients (75 M/25 F, mean age: 68±11 years), IRB approved, Toho University, Japan, consisted of Left/Right common carotid artery (CCA) artery (200 ultrasound scans), (Toshiba, Tokyo, Japan) were collected using a 7.5MHz transducer. The measured cIMTs for L/R carotid were as follows (in mm): (i) AtheroCloud™ (0.87±0.20, 0.77±0.20); (ii) sonographer (0.97±0.26, 0.89±0.29) and (iii) manual (0.90±0.20, 0.79±0.20), respectively. The coefficient of correlation (CC) between sonographer and manual for L/R cIMT was 0.74 (P<0.0001) and 0.65 (P<0.0001), while, between AtheroCloud™ and manual was 0.96 (P<0.0001) and 0.97 (P<0.0001), respectively. We observed that 91.15% of the population in AtheroCloud™ had a mean cIMT error less than 0.11mm compared to sonographer's 68.31%. The area under curve for receiving operating characteristics was 0.99 for AtheroCloud™ against 0.81 for sonographer. Our Framingham Risk Score stratified the population into three bins as follows: 39% in low-risk, 70.66% in medium-risk and 10.66% in high-risk bins. Statistical tests were performed to demonstrate consistency, reliability and accuracy of the results. The proposed AtheroCloud™ system is completely reliable, automated, fast (3-5 seconds depending upon the image size having an internet speed of 180Mbps), accurate, and an intelligent, web-based clinical tool for multi-center clinical trials and routine telemedicine clinical care.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Atherosclerosis; Automated; Carotid; Cloud; Pharmaceutical trails; Precision of merit; ROC; Routine; Stroke; Telemedicine; Ultrasound; Web-based; cIMT; reliable

Mesh:

Year:  2016        PMID: 27318571     DOI: 10.1016/j.compbiomed.2016.06.010

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  6 in total

Review 1.  A Review on Atherosclerotic Biology, Wall Stiffness, Physics of Elasticity, and Its Ultrasound-Based Measurement.

Authors:  Anoop K Patel; Harman S Suri; Jaskaran Singh; Dinesh Kumar; Shoaib Shafique; Andrew Nicolaides; Sanjay K Jain; Luca Saba; Ajay Gupta; John R Laird; Argiris Giannopoulos; Jasjit S Suri
Journal:  Curr Atheroscler Rep       Date:  2016-12       Impact factor: 5.113

2.  Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models.

Authors:  Ankush Jamthikar; Deep Gupta; Luca Saba; Narendra N Khanna; Tadashi Araki; Klaudija Viskovic; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; Vijay Viswanathan; Aditya Sharma; Andrew Nicolaides; George D Kitas; Jasjit S Suri
Journal:  Cardiovasc Diagn Ther       Date:  2020-08

3.  Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on "Integrated Vascular Age" instead of "Chronological Age": a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts.

Authors:  Ankush Jamthikar; Deep Gupta; Elisa Cuadrado-Godia; Anudeep Puvvula; Narendra N Khanna; Luca Saba; Klaudija Viskovic; Sophie Mavrogeni; Monika Turk; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; George D Kitas; Chithra Shankar; Andrew Nicolaides; Vijay Viswanathan; Aditya Sharma; Jasjit S Suri
Journal:  Cardiovasc Diagn Ther       Date:  2020-08

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

Authors:  Luca Saba; Mainak Biswas; Harman S Suri; Klaudija Viskovic; John R Laird; Elisa Cuadrado-Godia; Andrew Nicolaides; N N Khanna; Vijay Viswanathan; Jasjit S Suri
Journal:  Cardiovasc Diagn Ther       Date:  2019-10

5.  Parameter Measurement of Live Animals Based on the Mirror of Multiview Point Cloud.

Authors:  Jinsong Zhang; Yue Qin; Li Wang
Journal:  Comput Intell Neurosci       Date:  2021-10-29

6.  Unseen Artificial Intelligence-Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study.

Authors:  Pankaj K Jain; Neeraj Sharma; Luca Saba; Kosmas I Paraskevas; Mandeep K Kalra; Amer Johri; John R Laird; Andrew N Nicolaides; Jasjit S Suri
Journal:  Diagnostics (Basel)       Date:  2021-12-02
  6 in total

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