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Cardiology/Vascular Diseases


Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study

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BACKGROUND: The death due to stroke is caused by embolism of the arteries which is due to the rupture of the atherosclerotic lesions in carotid arteries. The lesion formation is over time, and thus, early screening is recommended for asymptomatic and moderate-risk patients. The previous techniques adopted conventional methods or semi-automated and, more recently, machine learning solutions. A handful of studies have emerged based on solo deep learning (SDL) models such as UNet architecture. METHODS: The proposed research is the first to adopt hybrid deep learning (HDL) artificial intelligence models such as SegNet-UNet.


Future Of AI In Healthcare Industry

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Robot-assisted surgery is considered "minimally invasive" so patients won't need to heal from large incisions. Via artificial intelligence, robots can use data from past operations to inform new surgical techniques. Automated surgery is also fast becoming a ubiquitous reality. Virtual nurses will be available 24/7, they can answer the questions, monitor patients and provide quick answers. Virtual nurse assistants can even provide wellness checks through voice and AI.


@Radiology_AI

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"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To assess if semisupervised natural language processing (NLP) of text clinical radiology reports could provide useful automated diagnosis categorization for ground truth labeling to overcome manual labeling bottlenecks in the machine learning pipeline. In this retrospective study, 1503 text cardiac MRI reports (from between 2016 and 2019) were manually annotated for five diagnoses by clinicians: normal, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), myocardial infarction (MI), and myocarditis.


Making Artificial Intelligence Lemonade Out of Data Lemons: Adaptation of a Public Apical Echo Database for Creation of a Subxiphoid Visual Estimation Automatic Ejection Fraction Machine Learning Algorithm

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Assess feasibility of training ML algorithms to visually estimate left ventricular ejection fraction (EF) from a subxiphoid (SX) window using only apical 4-chamber (A4C) images. METHODS: Researchers used a long-short-term-memory algorithm for image analysis. Using the Stanford EchoNet-Dynamic database of 10,036 A4C videos with calculated exact EF, researchers tested 3 ML training permeations. First, training on unaltered Stanford A4C videos, then unaltered and 90 clockwise (CW) rotated videos and finally unaltered, 90 rotated and horizontally flipped videos. As a real-world test, we obtained 615 SX videos from Harbor-UCLA (HUCLA) with EF calculations in 5% ranges.


Heartbeat Anomaly Detection

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According to a report of WHO, around 17.9 million people die each year due to Cardiovascular Diseases.Over the years it has been found that these deaths can be prevented if the diseases are diagnosed at an early stage and even the disease can be cured. Artificial Intelligence has been applied in various fields and one of them is AI for healthcare.We have seen AI practitioners coming up with solution for various disease diagnosis such as Cancer Detection, Detection of Diabetic Retinopathy and much more.The techniques used in these detections mostly involve Deep Learning. So, by combining our knowledge of deep learning and with its integration Iot we can develop a smart digital-stethoscope which can help in diagnosing anomalies in heartbeat in real-time and can help in classifying Cardio-diseases. While working in cAInvas one of its key features is UseCases Gallary.When working on any of its UseCases you don't have to look for data manually.As they have the feature to import your dataset to your workspace when you work on them.To load the data we just have to enter the following commands: As with all unstructured data formats, audio data has a couple of preprocessing steps which have to be followed before it is presented for analysis. Another way of representing audio data is by converting it into a different domain of data representation, namely the frequency domain.


Artificial Intelligence Driving Innovation in the United States Pacemaker Market

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SAN ANTONIO, Nov. 23, 2021 (GLOBE NEWSWIRE) -- A new analysis by Verify Markets shows the U.S. pacemaker market was valued over $1.7 billion in 2020 and is expected to witness a steady growth rate during the forecast period. The market is likely to be driven by a growing aging population, rising prevalence of cardiovascular and chronic diseases, and technological advances in pacemaker designs that reduce risk and discomfort. Some examples include the development of leadless pacemakers and MRI-compatible pacemakers. Artificial intelligence techniques have a potential to increase accuracy, reliability, and reduce errors in diagnostic methods, which are likely to help in early diagnosis and implementation of cost-effective treatments for cardiovascular diseases. Pacemakers are increasingly being designed to reduce patient discomfort and are equipped with long lasting batteries and remote accessibility.


AI IN HEALTH: Applications, challenges, limitations,

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What are possible applications for AI in this field, and how can we develop and use the technology in a way that is transparent and compatible with the public interest, while stimulating and driving innovation in the sector? BIOTOPIA is delighted to welcome a panel of experts from Helmholtz Center Munich.


Artificial intelligence–based method predicts risk of atrial fibrillation

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Atrial fibrillation--an irregular and often rapid heart rate--is a common condition that often leads to the formation of clots in the heart that can travel to the brain to cause a stroke. As described in a study published in Circulation, a team led by researchers at Massachusetts General Hospital (MGH) and the Broad Institute of MIT and Harvard has developed an artificial intelligence–based method for identifying patients who are at risk for developing atrial fibrillation and could therefore benefit from preventative measures. The investigators developed the artificial intelligence–based method to predict the risk of atrial fibrillation within the next five years based on results from electrocardiograms (noninvasive tests that record the electrical signals of the heart) in 45,770 patients receiving primary care at MGH. Next, the scientists applied their method to three large data sets from studies including a total of 83,162 individuals. The AI-based method predicted atrial fibrillation risk on its own and was synergistic when combined with known clinical risk factors for predicting atrial fibrillation. The method was also highly predictive in subsets of individuals such as those with prior heart failure or stroke.


Living Things: Breathing pattern

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This is something that we've done our entire life and still we haven't mastered it, this is due to countless variables, but one of those we could say that is the fast life we have in big cities. If we don't actively understand breathing, how could we simulate this for robots? Let's take a look into how breathing patterns works and how doctors actually learn in school. If you take a look in the video below, you'll see a few breathing patterns along with specific situations for each one of those. Those are some of the names gathered in medicine along the years to describe breathing patterns that we have as humans, but they're not exactly ready for usage as they do not relate specifically to a current emotion, and more likely to how our body is chemically/hormone related responding.


AI-CHD

Communications of the ACM

Congenital heart disease (CHD), the most common congenital birth defect, has long been known as one of the main causes of infant death during the first year of life.1 More than one million of the world's approximately 135 million newborns are born each year with CHD.21 Over the last century, cardiac surgery has been an effective approach to tackling CHD; its remarkable advance has decreased the mortality rate of newborns with CHD.10 However, that lower mortality rate is mostly observed in developed countries rather than developing ones. Surgical treatment of CHD requires highly skilled surgeons along with complex infrastructures and equipment. While developed countries have perfected their treatment of CHD for more than 50 years, developing countries are still in the early stages. It is estimated that the number of congenital cardiac surgeons needs to increase by 1,250 times to satisfy only the basic needs of CHD treatment worldwide,16 and most of those surgeons reside in developed countries. As a result, the mortality rate in developing countries is currently at 20%, strikingly higher than the 3% to 7% in developed countries,16 not to mention the fact that mortality rates in developing countries are likely underreported due to the lack of proper diagnosis. Remote surgery has been an active field for decades, enabling experienced surgeons to remotely instruct robots (telerobotics) or guide less-experienced surgeons (surgical telementoring).8