Artificial Intelligence

A University of Saskatchewan team has created a deep learning technique that demonstrated enhanced de-noising capabilities in low-dose CT (LDCT) imaging, resulting in little resolution loss and better performance, according to a study published in the Journal of Digital Imaging.

Radiation therapy is an integral part of many cancer treatments. Ideally, doses are focused on the observable tumor while leaving surrounding organs unaffected, but determining the figuration of tumors and organs-at-risk is done manually—a time consuming and, at times, imprecise task for radiologists.

A new broad oncology deep learning suite from the cloud-based medical imaging software solutions company Arterys Inc. was approved for 501(k) clearance by the FDA, according to a report by Business Insider.  

Through functional MRI (fMRI) and machine learning technology, UCLA researchers have developed a way to predict whether individuals with obsessive compulsive disorder (OCD) will benefit from cognitive behavioral therapy (CBT), according to a recent UCLA release.  

According to a Feb. 13 press release, the FDA announced clearance for the marketing of the Viz.AI Contact application, a clinical decision support software created to analyze CT results and notify providers of a potential stroke.

According to a December 2017 research survey conducted by the healthcare market research firm Reaction Data, most hospitals and imaging centers will be using machine learning or artificial intelligence (AI) technology by 2020.

As industry experts continue to explore artificial intelligence (AI) applications in radiology, the question remains of whether AI applications can and will add value, including in new knowledge and information to provide patients with better outcomes at lower costs.

Artificial intelligence and machine learning are all the rage—and for good reason. But researchers claim the brain doesn’t actually use the regions identified by machine learning to perform a task. Rather, these algorithms reflect the mental associations related to the task.

As medical imaging continues to evolve, Health Imaging spoke with enterprise imaging and health informatics expert Paul Chang, MD, professor of radiology and vice chair rad informatics at the University of Chicago Medicine, about what practitioners and healthcare technology leaders should keep in mind regarding artificial intelligence (AI) and deep learning in the coming year.  

Myelin, a fatty substance that sheaths nerves, is often damaged or missing in individuals with multiple sclerosis (MS). Physicians have depended on MR to detect demyelination, but the modality is incapable of differentiating between lost or damaged myelin and inflammation.

A research team with Boston University of Medicine has demonstrated artificial intelligence (AI) can analyze kidney biopsy images more accurately than traditional human methods.

MRI image analysis and machine learning may be a more accurate and efficient when applied to fetal MRI findings to predict the need for postnatal cerebrospinal fluid (CSF) diversion, according to a retrospective analysis study recently published by JAMA. According the study, researchers were able to develop a prognostic information model that can guide a more efficient candidate selection for potential fetal surgery.