Source: hitinfrastructure.com Two projects sponsored by Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) have generated solid use cases for machine learning in clinical care. Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) collaborated in August 2019 to advance innovation in areas including cancer diagnostics, precision medicine, electronic health records, and Read More
Tag: Medical Imaging
Source: healthitanalytics.com Researchers at the University of Iowa (UI) have received a $1 million grant from the National Science Foundation (NSF) to develop a machine learning platform to train algorithms with data from around the world. The phase one grant will enable the UI team to lead a multi-university and industry collaboration and address concerns Read More
Source: healthitanalytics.com A deep learning system was able to choose the most high-quality embryos for in-vitro fertilization (IVF) with 90 percent accuracy, according to a study published in eLife. When compared with trained embryologists, the deep learning model performed with an accuracy of approximately 75 percent while the embryologists performed with an average accuracy of 67 percent. The Read More
Source: healthitanalytics.com January 28, 2020 – New research from UT Southwestern has shown that deep learning technology could help providers quickly develop optimal treatment plans for cancer patients, decreasing the odds that the disease will spread. Patients usually have to wait several days to a week to begin therapy while doctors manually develop treatment plans, which can be a Read More
Source: pubs.rsna.org Deep learning with convolutional neural networks (CNNs) has shown tremendous success in classifying images, as we have seen with the ImageNet competition (1), which consists of millions of everyday color images, such as animals, vehicles, and natural objects. For example, recent artificial intelligence (AI) systems have achieved a top-five accuracy (correct answer within Read More
Source: venturebeat.com Medical imaging is among the most popular application of AI and machine learning, and with good reason. Computer vision algorithms are naturally adept at spotting anomalies experts sometimes miss, in the process reducing wait times and lightening clinical workloads. Perhaps that’s why although the percentage of health care organizations that have adopted AI Read More
Source: dqindia.com November 08, 2019 – Machine learning methods could accurately identify cancerous esophagus tissue on microscopy images without the time-consuming manual data input that is required for current methods, according to a study published in JAMA Network Open. Researchers at Dartmouth and Dartmouth-Hitchcock Norris Cotton Cancer Center have developed an innovative machine learning approach that automatically learns clinically important Read More
Source: healthitanalytics.com November 04, 2019 – What do the following numbers have to do with the annual meeting of the Radiological Society of North America: 2, 12, 32, 271, and 308? They refer to the presence of “artificial intelligence” at the show from 2015 to 2019, in that order. RNSA sees tremendous potential in the application of Read More
Source: healthitanalytics.com The Pittsburgh Health Data Alliance (PHDA) is partnering with Amazon Web Services (AWS) to improve medical imaging, cancer diagnostics, precision medicine, voice-enabled technologies, and other areas of healthcare with machine learning. The AWS Machine Learning Research sponsorship will enable PHDA scientists from the University of Pittsburgh and Carnegie Mellon University (CMU) to accelerate research and Read More
Source- healthitanalytics.com Medical imaging and machine learning are on a collision course that promises significant advancements in diagnostics and precision medicine, according to a new report from Frost & Sullivan. Advances in artificial intelligence and powerful computing technologies to support highly detailed imaging studies are driving opportunities for vendors and providers to capture a segment of a quickly Read More