Machine learning can accurately predict survival after echocardiography by analyzing unique data produced from heart images and electronic health record (EHR) information, according to a June 13 study in the Journal of the American College of Cardiology: Cardiovascular Imaging.
Researchers utilized five different machine learning approaches to accurately spot lymphedema—a negative side effect of breast cancer treatment—which may help detect it earlier and improve treatment.
Research presented at ASCO 2018 found that using contrast perfusion-weighted MRI enhanced by artificial intelligence (AI) and texture analysis can differentiate between brain tumors according to their mutation status.
“The most striking thing to me as a researcher crafting these attacks was probably how easy they were to carry out," said study lead author Samuel Finlayson, a computer scientist and biomedical informatician at Harvard Medical School in Boston, in an IEEE Spectrum story.
As artificial intelligence (AI) is poised to change transportation with self-driving vehicles, Kimberly Powell, with the Nvidia Corporation, and colleagues believe their work on the subject can also be applied to radiology.
The Society for Imaging Informatics in Medicine’s 2018 annual meeting wrapped up June 2 with a keynote address from Curt Langlotz, MD, PhD, with Stanford University, on the rise of artificial intelligence (AI).
A professor of radiology at Johns Hopkins Medicine in Baltimore and computer science students are developing a tumor-detecting AI algorithm that can be built into computed tomography (CT) scanner software to recognize and differentiate between a normal pancreas and one that's cancerous.
When compared to the performance of 58 dermatologists from 17 different countries around the world, AI missed fewer melanomas and misdiagnosed benign moles less often, according to a study published in the Annals of Oncology.
Theresa May, Prime Minister of the United Kingdom, has pledged millions toward government funding that will develop a "new weapon"—artificial intelligence (AI) able to improve cancer and chronic disease diagnosis.
Researchers utilized a machine learning algorithm to determine that a higher rate of change—rather than actual value of cancer antigen 125 (CA125)—is associated with abdominal recurrence of ovarian cancer. Findings may help identify patients most likely to benefit from imaging surveillance of the disease.
University College London Hospital (UCLH) and the Alan Turing Institute in London have entered a three-year partnership to allow artificial intelligence (AI) to perform a variety of clinical tasks otherwise done by nurses and physicians.
In a recent paper from consulting firm Deloitte, experts argue that evolving digital technology—notably artificial intelligence (AI)—has the potential to create jobs in many areas of healthcare, including diagnostic radiology.
The Kimia Lab at the University of Waterloo in Ontario, Canada, announced it has received $3.7 million from the Ontario Research Fund-Research Excellence program for its artificial intelligence (AI) search engine project in digital pathology.
Researchers at Imperial College London and the University of Edinburgh in Scotland developed a machine learning software able to detect small vessel disease, a common cause of dementia and stroke, in brain CT scans with almost 90 percent accuracy.
South Korea is the latest nation to incorporate artificial intelligence (AI) in their healthcare system. Today, South Korea's Ministry of Food and Drug Safety approved its first AI-based medical device, according to an article published May 16 by the Korea Herald.
Researchers have found that quantitative radiomics can better distinguish between benign lesions and luminal A breast cancers than using maximum linear size alone, according to a study published May 10 in Academic Radiology.
“Today there are 34,000 radiologists in the United States. Unless radiologists do things other than interpret imaging studies, there will be need for far fewer of them,” wrote Robert Schier, MD, with Radnet, in the Journal of the American College of Radiology.
Contouring is an instrumental process for radiation oncologists and their patients—but the method is highly subjective. Researchers found their deep neural network algorithm could result in massive time savings for providers.
Bibb Allen Jr., MD, chief medical officer with the American College of Radiology (ACR)’s Data Science Institute (DSI) talked with HealthImaging about focusing Medical Image Computing and Computer Assistance Intervention's (MICCAI) artificial intelligence (AI) challenges on radiologists' clinical needs.
Using genomic sequencing to identify mutations in emerging strains of bacteria responsible for or at-risk of causing an outbreak is time consuming and labor-intensive. However, researchers have recently developed a new machine learning tool that can speed up the process.
Researchers have found a new method utilizing established data sets and a machine learning algorithm that can outperform traditional methods of diagnosing autism spectrum disorders (ASD) in young children, which could improve accuracy in early diagnosis.
