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Consider the Promises and Challenges of Medical Image Analyses Using Machine Learning
Medical imaging saves millions of lives each year, helping doctors detect and diagnose a wide range of diseases, from cancer and appendicitis to stroke and heart disease. Because non-invasive early disease detection saves so many lives, scientific investment continues to increase. Artifical intelligence (AI) has the potential to revolutionize the medical imaging industry by sifting through mountains of scans quickly and offering providers and patients with life-changing insights into a variety of diseases, injuries, and conditions that may be hard to detect without the supplemental technology. Images are the largest source...
Source: MDDI - June 2, 2020 Category: Medical Devices Authors: Partha S. Anbil and Michael T. Ricci Tags: Imaging Source Type: news

Cardiovascular/Stroke Risk Prevention: A New Machine Learning Framework Integrating Carotid Ultrasound Image-based Phenotypes and its Harmonics with Conventional Risk Factors
Publication date: Available online 18 June 2020Source: Indian Heart JournalAuthor(s): Ankush Jamthikar, Deep Gupta, Narendra N. Khanna, Luca Saba, John R. Laird, Jasjit S. Suri
Source: Indian Heart Journal - June 18, 2020 Category: Cardiology Source Type: research

Brain lesions on DWI-MRI linked to poor outcomes for some
Lesions that appear on diffusion-weighted imaging (DWI) MRI scans represent...Read more on AuntMinnie.comRelated Reading: Deep-learning model detects cerebral microbleeds on MRI Key MRI markers link vascular brain injury to health risks 5 risk factors help predict brain hemorrhage on CT Combination of ultrasound, tPA may boost stroke treatment CT guidance helps brain hemorrhage outcomes
Source: AuntMinnie.com Headlines - July 21, 2020 Category: Radiology Source Type: news

Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system
AbstractVisual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of this study is to develop and design an automated carotid plaque characterization and classification system into binary classes, namely symptomatic and asymptomatic types via the deep learning (DL) framework implemented on a supercomputer. We hypothesize that on ultrasound images, symptomatic carotid plaques have (a) a low grayscale median because of a histologically large lipid core and relatively little collagen and calcium, and (b) a higher chaotic (heterogeneous) gra...
Source: The International Journal of Cardiovascular Imaging - January 9, 2021 Category: Radiology Source Type: research

Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: an observational study, British journal of anaesthesia
Conclusions: Machine learning generated several models for predicting fluid responsiveness that were comparable with the haemodynamic response to PLR.
Source: Current Awareness Service for Health (CASH) - May 6, 2021 Category: Consumer Health News Source Type: news

Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study
CONCLUSIONS: The proposed study demonstrates a fast, accurate, and reliable solution for early detection and quantification of plaque lesions in common carotid artery ultrasound scans. The system runs on a test US image in < 1 second, proving overall performance to be clinically reliable.PMID:34825801 | DOI:10.23736/S0392-9590.21.04771-4
Source: International Angiology - November 26, 2021 Category: Cardiology Authors: Pankaj K Jain Neeraj Sharma Luca Saba Kosmas I Paraskevas Mandeep K Kalra Amer Johri Andrew N Nicolaides Jasjit S Suri Source Type: research

Multicomponent material property characterization of atherosclerotic human carotid arteries through a Bayesian Optimization based inverse finite element approach
CONCLUSION: The developed iFEM approach combined with the inverted GP model successfully predicted component-wise material properties of intact atherosclerotic human carotids ex-vivo under physiological-like loading conditions.SIGNIFICANCE: We developed a novel iFEM framework for the nonlinear, component-wise material characterization of atherosclerotic arteries and utilized it to obtain human atherosclerotic carotid material properties. The developed iFEM framework has great potential to be advanced for patient-specific in-vivo application.PMID:34864574 | DOI:10.1016/j.jmbbm.2021.104996
Source: Journal of the Mechanical Behavior of Biomedical Materials - December 5, 2021 Category: Materials Science Authors: Su Guvenir Torun Hakki M Torun Hendrik H G Hansen Chris L de Korte Antonius F W van der Steen Frank J H Gijsen Ali C Akyildiz Source Type: research

Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study
CONCLUSIONS: The proposed study demonstrates a fast, accurate, and reliable solution for early detection and quantification of plaque lesions in common carotid artery ultrasound scans. The system runs on a test US image in < 1 second, proving overall performance to be clinically reliable.PMID:34825801 | DOI:10.23736/S0392-9590.21.04771-4
Source: International Angiology - November 26, 2021 Category: Cardiology Authors: Pankaj K Jain Neeraj Sharma Luca Saba Kosmas I Paraskevas Mandeep K Kalra Amer Johri Andrew N Nicolaides Jasjit S Suri Source Type: research

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