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youngscientist96 · 5 months ago

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Can deep learning transform heart failure prevention

A deep neural network called CHAIS may soon replace invasive procedures like catheterization as the new gold standard for monitoring heart health.

The ancient Greek philosopher and polymath Aristotle once concluded that the human heart is tri-chambered and that it was the single most important organ in the entire body, governing motion, sensation, and thought.

Today, we know that the human heart actually has four chambers and that the brain largely controls motion, sensation, and thought. But Aristotle was correct in observing that the heart is a vital organ, pumping blood to the rest of the body to reach other vital organs. When a life-threatening condition like heart failure strikes, the heart gradually loses the ability to supply other organs with enough blood and nutrients that enables them to function.

Researchers from MIT and Harvard Medical School recently published an open-access , introducing a noninvasive deep learning approach that analyzes electrocardiogram (ECG) signals to accurately predict a patient’s risk of developing heart failure. In a clinical trial, the model showed results with accuracy comparable to gold-standard but more-invasive procedures, giving hope to those at risk of heart failure. The condition has recently seen in mortality, particularly among young adults, likely due to the growing prevalence of obesity and diabetes.

“This paper is a culmination of things I’ve talked about in other venues for several years,” says the paper’s senior author Collin Stultz, director of and affiliate of the (Jameel Clinic). “The goal of this work is to identify those who are starting to get sick even before they have symptoms so that you can intervene early enough to prevent hospitalization.”

Of the heart’s four chambers, two are atria and two are ventricles — the right side of the heart has one atrium and one ventricle, and vice versa. In a healthy human heart, these chambers operate in a rhythmic synchrony: oxygen-poor blood flows into the heart via the right atrium. The right atrium contracts and the pressure generated pushes the blood into the right ventricle where the blood is then pumped into the lungs to be oxygenated. The oxygen-rich blood from the lungs then drains into the left atrium, which contracts, pumping the blood into the left ventricle. Another contraction follows, and the blood is ejected from the left ventricle via the aorta, flowing into veins branching out to the rest of the body.

“When the left atrial pressures become elevated, the blood drain from the lungs into the left atrium is impeded because it’s a higher-pressure system,” Stultz explains. In addition to being a professor of electrical engineering and computer science, Stultz is also a practicing cardiologist at Mass General Hospital (MGH). “The higher the pressure in the left atrium, the more pulmonary symptoms you develop — shortness of breath and so forth. Because the right side of the heart pumps blood through the pulmonary vasculature to the lungs, the elevated pressures in the left atrium translate to elevated pressures in the pulmonary vasculature.”

The current gold standard for measuring left atrial pressure is right heart catheterization (RHC), an invasive procedure that requires a thin tube (the catheter) attached to a pressure transmitter to be inserted into the right heart and pulmonary arteries. Physicians often prefer to assess risk noninvasively before resorting to RHC, by examining the patient’s weight, blood pressure, and heart rate.

But in Stultz’s view, these measures are coarse, as evidenced by the fact that “What we are seeking is something that gives you information like that of an invasive device, other than a simple weight scale,” Stultz says.

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sunaleisocial · 5 months ago

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Can deep learning transform heart failure prevention?

New Post has been published on https://sunalei.org/news/can-deep-learning-transform-heart-failure-prevention/

Can deep learning transform heart failure prevention?

Researchers from MIT and Harvard Medical School recently published an open-access paper in Nature Communications Medicine, introducing a noninvasive deep learning approach that analyzes electrocardiogram (ECG) signals to accurately predict a patient’s risk of developing heart failure. In a clinical trial, the model showed results with accuracy comparable to gold-standard but more-invasive procedures, giving hope to those at risk of heart failure. The condition has recently seen a sharp increase in mortality, particularly among young adults, likely due to the growing prevalence of obesity and diabetes.

“This paper is a culmination of things I’ve talked about in other venues for several years,” says the paper’s senior author Collin Stultz, director of Harvard-MIT Program in Health Sciences and Technology and affiliate of the MIT Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic). “The goal of this work is to identify those who are starting to get sick even before they have symptoms so that you can intervene early enough to prevent hospitalization.”

But in Stultz’s view, these measures are coarse, as evidenced by the fact that one-in-four heart failure patients is readmitted to the hospital within 30 days. “What we are seeking is something that gives you information like that of an invasive device, other than a simple weight scale,” Stultz says.

In order to gather more comprehensive information on a patient’s heart condition, physicians typically use a 12-lead ECG, in which 10 adhesive patches are stuck onto the patient and linked with a machine that produces information from 12 different angles of the heart. However, 12-lead ECG machines are only accessible in clinical settings and they are also not typically used to assess heart failure risk.

Instead, what Stultz and other researchers propose is a Cardiac Hemodynamic AI monitoring System (CHAIS), a deep neural network capable of analyzing ECG data from a single lead — in other words, the patient only needs to have a single adhesive, commercially-available patch on their chest that they can wear outside of the hospital, untethered to a machine.

To compare CHAIS with the current gold standard, RHC, the researchers selected patients who were already scheduled for a catheterization and asked them to wear the patch 24 to 48 hours before the procedure, although patients were asked to remove the patch before catheterization took place. “When you get to within an hour-and-a-half [before the procedure], it’s 0.875, so it’s very, very good,” Stultz explains. “Thereby a measure from the device is equivalent and gives you the same information as if you were cathed in the next hour-and-a-half.”

“Every cardiologist understands the value of left atrial pressure measurements in characterizing cardiac function and optimizing treatment strategies for patients with heart failure,” says Aaron Aguirre SM ’03, PhD ’08, a cardiologist and critical care physician at MGH. “This work is important because it offers a noninvasive approach to estimating this essential clinical parameter using a widely available cardiac monitor.”

Aguirre, who completed a PhD in medical engineering and medical physics at MIT, expects that with further clinical validation, CHAIS will be useful in two key areas: first, it will aid in selecting patients who will most benefit from more invasive cardiac testing via RHC; and second, the technology could enable serial monitoring and tracking of left atrial pressure in patients with heart disease. “A noninvasive and quantitative method can help in optimizing treatment strategies in patients at home or in hospital,” Aguirre says. “I am excited to see where the MIT team takes this next.”

But the benefits aren’t just limited to patients — for patients with hard-to-manage heart failure, it becomes a challenge to keep them from being readmitted to the hospital without a permanent implant, taking up more space and more time of an already beleaguered and understaffed medical workforce.

The researchers have another ongoing clinical trial using CHAIS with MGH and Boston Medical Center that they hope to conclude soon to begin data analysis.

“In my view, the real promise of AI in health care is to provide equitable, state-of-the-art care to everyone, regardless of their socioeconomic status, background, and where they live,” Stultz says. “This work is one step towards realizing this goal.”

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lightgreyartgallery · 6 years ago

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Tasteful Nudes Innuendo # LOVESONA RSVP HERE ••• February 22nd from 7-10pm at Light Grey Art Lab Free and open to the public Prepare your senses, and your heart, for a steamy collection of the suggestively quirky and the salaciously spicy. Like a sip of red wine on satin sheets, Light Grey Art Lab’s February exhibition promises to be a perfect event for a romantic rendezvous or self-love celebration. Concepts of love, the body, and art have always coexisted - with great emotion comes great art, and naturally, creators frequently work to find unique ways to express these complex and beautiful facets of being a human. The 3 new collections opening this February at Light Grey Art Lab feature an eclectic range of original illustrative works and artist produced goods, featuring the aesthetics of the figure, the humor of taboo, and a collection of illustrated dating profiles. Tasteful Nudes: The delicate curves of a body emerge through the settling dust of chalk pastel. The artist peers over their drawing board at a trusted model, reclining elegantly on their vintage lounge sofa. Their masterpiece is complete… An exploration capturing the inherent beauty of the human form. The Tasteful Nudes Exhibition includes 50+ creatives and is a celebration of the sensual, the subtle, and the sensory aspects of the human form. From sexy self-portraits and boudoir-esque paintings, to romantic reclining poses, to suggestive silhouettes, 'Tasteful' is the key word! Works include original drawings, digital illustrations, and more. Innuendo: It’s 7th period in high school, you’re in health class, and the teacher brings out the inevitable props… A banana, a donut, and all sorts of properly conservative allusions to the body. The class snickers, and for the rest of the day, innuendos are passed from friend to friend under the probing ears of the teachers. Innuendo is a collaborative exhibition of artwork and goods featuring playful allusions to the lewd and salacious. 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Participants in the # Lovesona project draw a portrait of themselves or a creative friend, write a bio about their/their friends’ deepest passions and interests, and then post it with the hashtag # lovesona! Along with these profiles, we’ll also be creating interactive content and activities on the Lovesona instagram, to help people dive deeper into what makes their Lovesona unique. Throughout the month of February, Light Grey will be reposting these profiles on our @my.lovesona instagram, where we hope people will connect and, who knows, maybe even fall in love... Tasteful Nudes Artists: Lillian Duermeier, Carmen Chow, Rachel Quast, Christine Griffin, Varsam Kurnia, Kring Demetrio, Grace Kim, Patricia Thomasson, Kristin Vogel, Kristen Acampora, Ashley Floréal, Sarah Hudkins, Paige Carpenter, Jesse Lindhorst, Reiko Murakami, Gica Tam, Chelsea Harper, Chrissy Curtin, Cleonique Hilsaca, Shelby Hacker, Aimee Fleck, Laura Galli, Caroline Dougherty, Ejiwa Ebenebe, Chelsea Marquette, Sandra Brandstätter, Rafael Mayani, Jo Yeh, Diana Van Damme, seosamh, Christopher Hegland, Kristin Siegel-Leicht, Sara Pace, Helen Mask, Daniel Gray-Barnett, Tidawan Thaipinnarong, Lydia Guadagnoli, Savannah Schroll Guz, JB Casacop, Jasmin Dreyer, Jimmy Malone, Jess Schultz, Micaela Dawn, Adriana Bellet, Lucas Durham, Stephen Wood, Saleha Chowdhury, Nadia Rausa, Sheena Klimoski, Victoria Roden, Natalie Shaw, Primary Hughes, Calvin A. Innuendo Artists: Ama Teibel, Andrea Pereira, Angela Bardakjian, Anne Passchier, Anouk van der Meer, Ashley Nordan, B. 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innonurse · 4 years ago

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#ai #artificial intelligence #machine learning #health tech #ethics #inclusiveness

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dorcasrempel · 5 years ago

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Technique reveals whether models of patient risk are accurate

After a patient has a heart attack or stroke, doctors often use risk models to help guide their treatment. These models can calculate a patient’s risk of dying based on factors such as the patient’s age, symptoms, and other characteristics.

While these models are useful in most cases, they do not make accurate predictions for many patients, which can lead doctors to choose ineffective or unnecessarily risky treatments for some patients.

“Every risk model is evaluated on some dataset of patients, and even if it has high accuracy, it is never 100 percent accurate in practice,” says Collin Stultz, a professor of electrical engineering and computer science at MIT and a cardiologist at Massachusetts General Hospital. “There are going to be some patients for which the model will get the wrong answer, and that can be disastrous.”

Stultz and his colleagues from MIT, IBM Research, and the University of Massachusetts Medical School have now developed a method that allows them to determine whether a particular model’s results can be trusted for a given patient. This could help guide doctors to choose better treatments for those patients, the researchers say.

Stultz, who is also a professor of health sciences and technology, a member of MIT’s Institute for Medical Engineering and Sciences and Research Laboratory of Electronics, and an associate member of the Computer Science and Artificial Intelligence Laboratory, is the senior author of the new study. MIT graduate student Paul Myers is the lead author of the paper, which appears today in Digital Medicine.

Modeling risk

Computer models that can predict a patient’s risk of harmful events, including death, are used widely in medicine. These models are often created by training machine-learning algorithms to analyze patient datasets that include a variety of information about the patients, including their health outcomes.

While these models have high overall accuracy, “very little thought has gone into identifying when a model is likely to fail,” Stultz says. “We are trying to create a shift in the way that people think about these machine-learning models. Thinking about when to apply a model is really important because the consequence of being wrong can be fatal.”

For instance, a patient at high risk who is misclassified would not receive sufficiently aggressive treatment, while a low-risk patient inaccurately determined to be at high risk could receive unnecessary, potentially harmful interventions.

To illustrate how the method works, the researchers chose to focus on a widely used risk model called the GRACE risk score, but the technique can be applied to nearly any type of risk model. GRACE, which stands for Global Registry of Acute Coronary Events, is a large dataset that was used to develop a risk model that evaluates a patient’s risk of death within six months after suffering an acute coronary syndrome (a condition caused by decreased blood flow to the heart). The resulting risk assessment is based on age, blood pressure, heart rate, and other readily available clinical features.

The researchers’ new technique generates an “unreliability score” that ranges from 0 to 1. For a given risk-model prediction, the higher the score, the more unreliable that prediction. The unreliability score is based on a comparison of the risk prediction generated by a particular model, such as the GRACE risk-score, with the prediction produced by a different model that was trained on the same dataset. If the models produce different results, then it is likely that the risk-model prediction for that patient is not reliable, Stultz says.

“What we show in this paper is, if you look at patients who have the highest unreliability scores — in the top 1 percent — the risk prediction for that patient yields the same information as flipping a coin,” Stultz says. “For those patients, the GRACE score cannot discriminate between those who die and those who don’t. It’s completely useless for those patients.”

The researchers’ findings also suggested that the patients for whom the models don’t work well tend to be older and to have a higher incidence of cardiac risk factors.

One significant advantage of the method is that the researchers derived a formula that tells how much two predictions would disagree, without having to build a completely new model based on the original dataset.

“You don’t need access to the training dataset itself in order to compute this unreliability measurement, and that’s important because there are privacy issues that prevent these clinical datasets from being widely accessible to different people,” Stultz says.

Retraining the model

The researchers are now designing a user interface that doctors could use to evaluate whether a given patient’s GRACE score is reliable. In the longer term, they also hope to improve the reliability of risk models by making it easier to retrain models on data that include more patients who are similar to the patient being diagnosed.

“If the model is simple enough, then retraining a model can be fast. You could imagine a whole suite of software integrated into the electronic health record that would automatically tell you whether a particular risk score is appropriate for a given patient, and then try to do things on the fly, like retrain new models that might be more appropriate,” Stultz says.

The research was funded by the MIT-IBM Watson AI Lab. Other authors of the paper include MIT graduate student Wangzhi Dai; Kenney Ng, Kristen Severson, and Uri Kartoun of the Center for Computational Health at IBM Research; and Wei Huang and Frederick Anderson of the Center for Outcomes Research at the University of Massachusetts Medical School.

Technique reveals whether models of patient risk are accurate syndicated from https://osmowaterfilters.blogspot.com/

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