VUNO Presents State-of-the-Art AI Medical Imaging Technology at ECR 2021

By MedImaging International staff writers
Posted on 05 Mar 2021

Illustration

VUNO Inc. (Seoul, Korea) showcased its complete deep learning-based imaging offerings in its virtual booth at the 2021 European Congress of Radiology (ECR), one of the biggest annual events, featuring a series of industry symposia in radiology.

At this year’s ECR held online in Vienna, Austria from March 3-10, VUNO highlighted real world clinical value that its powerful AI technology has been able to successfully demonstrate in radiologists' workflow as well as in the patient journey. Visitors at the VUNO-Med booth were able to enjoy a unique virtual experience of interactive live and on-demand product demo sessions, personalized conversations and a virtual tour of VUNO's impressive line-up of best-in-class innovations that truly empower radiologists to do so much more than ever before.

The company demonstrated four CE marked main product lines that can be seamlessly integrated with PACS (Picture Archiving and Communication System), EMR (Electronic Medical Record) systems or any medical devices: VUNO Med®–LungCT AI™ that detects, locates, and quantifies pulmonary lung nodules on CT images; VUNO Med®-BoneAge™, designed to assist doctors in reading a hand X-ray image to assess the bone age of a child; VUNO Med®-Chest X-ray™ that supports physicians in the chest X-ray reading of common thoracic abnormalities on chest radiographs; and VUNO Med®-DeepBrain®, developed as a diagnostic support tool for degenerative brain diseases through brain parcellation & quantification on brain MR images

"I am very excited to present VUNO's AI solutions to the world at ECR 2021," Hyun-jun Kim, CEO of VUNO said, adding "taking this opportunity, I want to be able to reaffirm and expand our success across other markets through close collaboration with our existing and new global partners."

Related Links:
VUNO Inc.

Visit expo >

Post-Processing Imaging System

DynaCAD Prostate

Mammo DR Retrofit Solution

DR Retrofit Mammography

MRI System

nanoScan MRI 3T/7T

X-Ray Generator

Advantage Plus Generators

Latest ECR 2021 News

Channels

Radiography

view channel

Images in a 75-year-old woman who underwent routine screening mammography in 2022. Left mediolateral oblique views shown from prior screening mammograms: (A) 2015, (B) 2018, (C) 2019, and (D) 2021. In (E) 2022, a new mass developed in the left breast at the 6-o’clock position (arrow), with a (F) corresponding irregular mass at US (arrow). Subsequent US-guided core needle biopsy yielded invasive ductal carcinoma, grade 2. The deep learning 5-year risk scores gradually increased from 2.0 (2015) to 2.1 (2018), 3.4 (2019), 3.6 (2021), and 15.3 (2022). CMFN = centimeters from nipple, LT = left, TRANS = transverse (Photo courtesy of Radiological Society of North America)

AI Tool Predicts Five-Year Breast Cancer Risk from Mammograms

Breast cancer risk assessment during routine screening is difficult because many women who develop the disease have no known genetic mutations or family history. Static risk tools provide limited discrimination... Read more

Image: Screening mammograms and artificial intelligence (AI) score changes over time in two individuals with screen-detected cancer. Full-field digital mammograms show craniocaudal (top) and mediolateral (bottom) views of the left and right breast (Photo courtesy of Radiological Society of North America)

AI Mammography Tools Detect Early Breast Cancer Signs Years Before Diagnosis

Breast cancer screening aims to detect tumors before symptoms develop, but subtle mammographic changes can appear years before diagnosis and may be missed during routine reads. Delayed detection can lead... Read more

$Image: Kyushu University researchers use Dynamic Chest Radiography (DCR) to evaluate the severity of Pulmonary Regurgitation (PR), a common complication after treatment of the congenital heart disease Tetralogy of Fallot. The analysis only requires the patient to hold their breath for seven seconds for a sequence of images of their heart to be taken. The images are then analyzed and extracted into a waveform that can show the severity of PR. The new technique\\\'s detection accuracy of severe PR is 93%, with a sensitivity of 93% and a specificity of 94%. (Photo courtesty of Kyushu University/Yuzo Yamasaki)$

Rapid X-Ray Test Quantifies Pulmonary Regurgitation After Tetralogy of Fallot Repair

Tetralogy of Fallot is the most common cyanotic congenital heart defect and can leave patients with pulmonary valve regurgitation, a backward flow of blood into the right ventricle after repair.... Read more

Image credit: Shu-Han Chen / St. Paul’s Hospital / National Taiwan University

AI Tool Flags Osteoporosis Risk from Routine Chest X-Rays

Osteoporosis is a progressive loss of bone density that is often silent until a fracture occurs. Current screening frameworks concentrate on older women and select high-risk groups. Many men, younger adults,... Read more

MRI

view channel

International Study Assesses AI for Prostate Cancer MRI Interpretation

Prostate cancer is a leading cause of cancer morbidity in men, and accurate early diagnosis hinges on expert interpretation of prostate magnetic resonance imaging (MRI). Rapid adoption of MRI-first pathways... Read more

$Image: By training the AI with simulations, researchers can reconstruct brain images with high precision using only one-tenth of the data typically required in magnetic resonance imaging. The image shows a metric known as “fractional anisotropy”—an index of myelination—obtained using conventional methods and with the new approach (Photo courtesy of Instituto de Neurociencias UMH CSIC)$

Ultrasound

view channel

$Image: A cross-section through the brain reveals the symmetric architecture of the vascular network (cyan-green) and the tracer highlighting the glymphatic system (red-yellow) along the brain\'s surface and drainage routes—imaged through the intact skull with 3D-PAULM. (Photo courtesy of Duke University)$