We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




New AI Platform Helps Radiologists Diagnose Stroke Faster Using CT Scans

By MedImaging International staff writers
Posted on 18 Dec 2018
Print article
Image: The AI-CT Stroke Screening System is designed to help radiologists detect and diagnose stroke faster than ever (Photo courtesy of Infervision).
Image: The AI-CT Stroke Screening System is designed to help radiologists detect and diagnose stroke faster than ever (Photo courtesy of Infervision).
A new artificial intelligence- (AI) driven stroke detection solution could help radiologists detect and diagnose stroke faster than ever, resulting in patients receiving lifesaving treatment when time is of the essence.

Infervision (Beijing, China), a tech company that uses deep learning and AI to assist and improve medical image analysis, has launched the AI-CT Stroke Screening System. The new technology assists doctors in determining whether the patients have suffered either a hemorrhagic (bleeding) stroke or an ischemic (blood clot) stroke, in order to provide effective and faster treatment.

In hemorrhagic stroke patients, the AI-CT Stroke Screening System technology assists doctors in accurately and quickly determining whether a bleeding-type stroke has occurred, how much blood volume is involved, and the bleed location - all of which are crucial for deciding treatment options. In ischemic strokes, doctors typically use MRI scans for diagnosis, especially in the early stages of the stroke. However, this can often pose a problem as MRIs are not always available around the clock, and also require additional time for preparation and scanning. With the Infervision platform, doctors can take scans using the much more readily available CT machines and use the AI technology to reach a faster diagnosis and save more brain tissue through faster and more appropriate treatment.

In order to develop this diagnostic capability, the Infervision platform applied deep learning technology and trained several thousands of datasets of annotated medical images. The Infervision platform is currently being tested by radiologists at Beijing Tian Tan Hospital to diagnose the type, location, and severity of a patient's stroke. In addition to the AI-CT Stroke Screening System, Infervision had earlier introduced a platform to aid radiologists in reading chest CT and X-ray scans for detecting lung cancer and other cardiothoracic diseases. Known as AI-CT Lung Screening System and AI-DR Lung Screening System, the technology has been in use for more than a year at several top hospitals in China, which is witnessing a huge demand for radiology diagnoses along with a scarcity of radiologists. Infervision's technology improves the efficiency of radiologists by reducing the time required to read each CT and X-ray scan and enabling the doctors to focus their attention on malignant lesions or nodules.

"Stroke is the third leading cause of death and the leading cause of permanent disability and loss of independent life-years in Western countries. At Infervision, we are committed to helping doctors speed their diagnosis of stroke so patients can get the best and most appropriate treatment as fast as possible. We have built a deep learning algorithm team of almost 100 people fully committed to developing the most cutting-edge AI solutions to make this a reality," said Kuan Chen (CK), founder and CEO of Infervision. "This can be life changing for many patients."

Related Links:
Infervision

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Under Table Shield
3 Section Double Pivot Under Table Shield
Computed Tomography (CT) Scanner
Aquilion Serve SP
PACS Workstation
CHILI Web Viewer

Print article
Radcal

Channels

MRI

view channel
Image: PET/MRI can accurately classify prostate cancer patients (Photo courtesy of 123RF)

PET/MRI Improves Diagnostic Accuracy for Prostate Cancer Patients

The Prostate Imaging Reporting and Data System (PI-RADS) is a five-point scale to assess potential prostate cancer in MR images. PI-RADS category 3 which offers an unclear suggestion of clinically significant... Read more

Nuclear Medicine

view channel
Image: The new SPECT/CT technique demonstrated impressive biomarker identification (Journal of Nuclear Medicine: doi.org/10.2967/jnumed.123.267189)

New SPECT/CT Technique Could Change Imaging Practices and Increase Patient Access

The development of lead-212 (212Pb)-PSMA–based targeted alpha therapy (TAT) is garnering significant interest in treating patients with metastatic castration-resistant prostate cancer. The imaging of 212Pb,... Read more

General/Advanced Imaging

view channel
Image: The Tyche machine-learning model could help capture crucial information. (Photo courtesy of 123RF)

New AI Method Captures Uncertainty in Medical Images

In the field of biomedicine, segmentation is the process of annotating pixels from an important structure in medical images, such as organs or cells. Artificial Intelligence (AI) models are utilized to... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.