Clean Sweep Live Auction on Wed. May 1st. Click to view the full inventory

DOTmed Home MRI Oncology Ultrasound Molecular Imaging X-Ray Cardiology Health IT Business Affairs
News Home Parts & Service Operating Room CT Women's Health Proton Therapy Endoscopy HTMs Pediatrics
> This Story

starstarstarstarstar (1)
注册记数器 to rate this News Story
Forward Printable StoryPrint Comment




CT Homepage

First ultra high-res CT scan performed on US patient Scanner at UC Davis can image anatomy as small as 150 microns

Trice Imaging connects imaging devices of large chain healthcare provider Aleris Patients and physicians can view images on laptops, cell phones

Industrial hi-res X-ray yields greater insight into child abuse case Identified microscopic injuries that would not have been detected with standard CT

Reducing extravasations in CT contrast-enhanced IV injections Tips and best practices for administering better care

Researchers orchestrate malware attack to expose imaging vulnerabilities Deceived radiologists and AI algorithms into misdiagnoses

House bill would require Medicare to cover CT colonography Supporters say it would improve screening compliance and outcomes

New approach identifies lung cancer patients most likely to respond to chemotherapy Combines radiomics and CT image assessment

BSWH to install Glassbeam's CLEAN blueprint to leverage machine uptime Will include integrated CMMS software by EQ2

Machine learning reduces false positives for lung cancer in low-dose CT False positives occur at rate of 96 percent

Siemens to unveil its SOMATOM go.Top Cardiovascular Edition CT at ACC 19 Ideal for the cardiovascular outpatient setting

AI can detect neurological conditions
on CT scans in less than two
seconds, says study

Study: AI detects neurological issues on CT scans in under two seconds

John R. Fischer , Staff Reporter
Scanning for neurological conditions may take little more than one second using an AI platform designed to identify a broad range of these issues, according to a new first-of-its-kind study.

Researchers at Icahn School of Medicine at Mount Sinai found that identifying diseases took as little as 1.2 seconds by applying AI to CT scans of stroke, hemorrhage, hydrocephalus and other neurological problems. The study is the first to demonstrate a direct clinical application based on the application of AI in detecting a wide range of acute neurological conditions.

Story Continues Below Advertisement

RaySafe helps you avoid unnecessary radiation

RaySafe solutions are designed to minimize the need for user interaction, bringing unprecedented simplicity & usability to the X-ray room. We're committed to establishing a radiation safety culture wherever technicians & medical staff encounter radiation.

"With a total processing and interpretation time of 1.2 seconds, such a triage system can alert physicians to a critical finding that may otherwise remain in a queue for minutes to hours," senior author Eric Oermann, instructor in the department of neurosurgery at the Icahn School of Medicine at Mount Sinai, said in a statement. "We're executing on the vision to develop artificial intelligence in medicine that will solve clinical problems and improve patient care."

Utilizing 37,236 head CT scans, researchers trained a deep neural network to identify if an image consisted of critical or non-critical findings. They then tested the ability of the solution to detect and notify clinicians of a disease as it triaged scans based on severity in a blinded, randomized controlled trial in a simulated clinical imaging environment.

The study relied upon "weakly supervised learning approaches" constructed from the researcher’s expertise in natural language processing and large clinical data sets within the Mount Sinai Health System.

The solution demonstrated a 150 times shorter time span, on average, in pre-processing an image, running its interference method and, if necessary, sending out an alert, compared to the average reading time of an image by a physician.

The next phase of testing will involve enhanced computer labeling of CT scans under the guidance of "strongly supervised learning approaches." It also will include novel techniques for increasing the efficiency of data.

"The expression 'time is brain' signifies that rapid response is critical in the treatment of acute neurological illnesses, so any tools that decrease time to diagnosis may lead to improved patient outcomes," study co-author Joshua Bederson, professor and system chair for the department of neurosurgery at Mount Sinai Health System and clinical director of the neurosurgery simulation core, said in a statement.

Researchers estimate integration of these changes within the system to be completed within the next two years.

The study was performed by the Mount Sinai AI Consortium (AISINAI), a group of scientists, physicians and researchers focused on developing and applying AI for accurate diagnosis and improved patient care. It further supports a paper published earlier this year by the group in the journal, Radiology, on the use of natural language processing algorithms for identifying clinical concepts in radiology reports for CT scans.

The study is available in the journal, Natural Medicine.

CT Homepage

You Must Be Logged In To Post A Comment

Parts Hunter +EasyPay
对这个站点的通入和用途是受期限和条件我们支配 法律公告 & 保密性通知
物产和业主对 DOTmed.com,公司 Copyright ©2001-2019 DOTmed.com, Inc.