Deep neural networks identified potential adverse outcomes and atrial fibrillation from 12-lead ECGs that were originally interpreted as normal, according to new research presented at the American Heart Association Scientific Sessions. "Applications of machine learning and artificial intelligence techniques to problems in health care are increasingly common, but generally focus on diagnostic problems such as detecting features in an image of classifying a current diagnosis based on present features," Christopher M. Haggerty, PhD, assistant professor in the department of imaging science and innovation, and Brandon K. Fornwalt, MD, PhD, associate professor and director of the department of imaging science and innovation, both at Geisinger in Danville, Pennsylvania, told Healio. "Few studies have been able to apply machine learning to the task of predicting future events or patient outcomes. This work is among the first to demonstrate proof of concept for predicting a future patient event -- 1-year mortality -- with good performance based solely on 12-lead electrocardiography data." Sushravya M. Raghunath, PhD, math and computational scientist in the department of imaging science and innovation at Geisinger, and colleagues analyzed 1,775,926 12-lead resting ECGs of 397,840 patients from 34 years of archived medical records.

Artificial intelligence can be used to accurately examine electrocardiogram (ECG) test results, according to the findings of two preliminary studies being presented at the American Heart Association Scientific Sessions 2019 in Philadelphia, PA. In the first study, researchers evaluated 1.1 million ECGs that did indicate atrial fibrillation (AF) from more than 237,000 patients. They used specialized computational hardware to train a deep neutral network to assess 30,000 data points for each respective ECG. The results showed that approximately one in three people received an AF diagnosis within a year. Moreover, the model demonstrated the capacity for long-term prognostic significance as patients predicted to develop AF after one year had a 45% higher hazard rate in developing AF over a follow-up duration of 25-years compared to other patients.

Artificial intelligence can be used to accurately examine electrocardiogram (ECG) test results, according to the findings of two preliminary studies to be presented at the American Heart Association's Scientific Sessions 2019 November 16-18 in Philadelphia. In the first study, researchers evaluated 1.1 million ECGs that indicated atrial fibrillation (AF) from more than 237,000 patients. They used specialized computational hardware to train a deep neutral network to assess 30,000 data points for each respective ECG. The results showed that approximately one in three people received an AF diagnosis within a year. Moreover, the model demonstrated the capacity for long-term prognostic significance as patients predicted to develop AF after one year had a 45% higher hazard rate in developing AF over a follow-up duration of 25-years compared to other patients.

Here's a scenario scary enough to warrant a horror movie: An artificial intelligence that is able to accurately predict your chances of dying in the next year by looking at heart test results, despite the fact that the results may look totally fine to trained doctors. The good news: The technology might just wind up saving your life one day. "We have developed two different artificial intelligence algorithms that can automatically analyze electrical tracings from the heart and make predictions about the likelihood of a future important clinical event," Brandon Fornwalt, from Pennsylvania-based healthcare provider Geisinger, told Digital Trends. In addition to the likelihood of death in a year, the algorithms can also predict the development of an abnormal heart rhythm called atrial fibrillation. The neural networks were trained on a data set consisting of 1.77 million electrocardiogram (ECG) results from close to 400,000 people.

Researchers led by Brandon Fornwalt at the Pennsylvania-based Geisinger Health System put their machine learning model to work studying the results of some 1.8 million electrocardiogram (ECG) heart scans, hoping the neural network would derive patterns from the heaps of data. Predicting the risk of a heart attack or other heart-related issues, the AI performed better than its human counterparts, consistently scoring above flesh-and-blood doctors. Even for ECG results that cardiologists determined to be normal, the AI was able to pick up on other patterns and accurately predict fatal health risks within a year's time. "That finding suggests that the model is seeing things that humans probably can't see, or at least that we just ignore and think are normal," Fornwalt said. AI can potentially teach us things that we've been maybe misinterpreting for decades.

After looking at standard ECG tests, Artificial Intelligence (AI) can help identify patients most likely to die of any medical cause within a year, claim researchers. To reach this conclusion, researchers from Geisinger Health System in Pennsylvania analysed the results of 1.77 million ECGs and other records from almost 400,000 patients. The team used this data to compare machine learning-based models that either directly analysed the raw ECG signals or relied on aggregated human-derived measures (standard ECG features typically recorded by a cardiologist) and commonly diagnosed disease patterns. The neural network model that directly analysed the ECG signals was found to be superior for predicting one-year risk of death. Surprisingly, the neural network was able to accurately predict risk of death even in patients deemed by a physician to have a normal ECG.

Dr Fornwalt added: 'That finding suggests that the model is seeing things that humans probably can't see, or at least that we just ignore and think are normal. 'AI can potentially teach us things that we've been maybe misinterpreting for decades.' Introduction of AI in such situations could see the rise of the superhuman doctor - however it is not known what rhythms the AI has detected, which makes the unexplained diagnosis a little unethical in some doctors opinions. The findings will be presented at the American Heart Association's Scientific Sessions in Dallas, U.S, on November 16 - with researchers hopeful that they will be able to prove its significance with clinical trial. AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn. ANNs can be trained to recognise patterns in information - including speech, text data, or visual images - and are the basis for a large number of the developments in AI over recent years. Conventional AI uses input to'teach' an algorithm about a particular subject by feeding it massive amounts of information. AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn.

Researchers from Pennsylvania healthcare provider Geisinger have trained an AI to predict which patients are at a higher risk of dying within the next year, New Scientist reports. They fed the AI 1.77 million electrocardiogram (ECG) logs -- measured in voltage over time -- from 400,000 patients, in order to detect patterns that could indicate future cardiac problems including heart attacks and atrial fibrillation. The results were impressive and a little scary. The AI model performed better than existing methods, according to the researchers, at distinguishing between patients who would die within a year and those who survived. "No matter what, the voltage-based model was always better than any model you could build out of things that we already measure from an ECG," Brandon Fornwalt, lead researcher of the study at Geisinger, told New Scientist.

New York– After looking at standard ECG tests, Artificial Intelligence (AI) can help identify patients most likely to die of any medical cause within a year, claim researchers. To reach this conclusion, researchers from Geisinger Health System in Pennsylvania analyzed the results of 1.77 million ECGs and other records from almost 400,000 patients. The team used this data to compare machine learning-based models that either directly analyzed the raw ECG signals or relied on aggregated human-derived measures (standard ECG features typically recorded by a cardiologist) and commonly diagnosed disease patterns. The neural network model that directly analyzed the ECG signals was found to be superior for predicting one-year risk of death. Surprisingly, the neural network was able to accurately predict risk of death even in patients deemed by a physician to have a normal ECG.

New York, After looking at standard ECG tests, Artificial Intelligence (AI) can help identify patients most likely to die of any medical cause within a year, claim researchers. To reach this conclusion, researchers from Geisinger Health System in Pennsylvania analyzed the results of 1.77 million ECGs and other records from almost 400,000 patients. The team used this data to compare machine learning-based models that either directly analyzed the raw ECG signals or relied on aggregated human-derived measures (standard ECG features typically recorded by a cardiologist) and commonly diagnosed disease patterns. The neural network model that directly analyzed the ECG signals was found to be superior for predicting one-year risk of death. Surprisingly, the neural network was able to accurately predict risk of death even in patients deemed by a physician to have a normal ECG.