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.

HospiMedica

Download Mobile App
Recent News AI Critical Care Surgical Techniques Patient Care Health IT Point of Care Business Focus

New Real-Time Imaging AI Platform Unveiled

By HospiMedica International staff writers
Posted on 29 Nov 2017
Lunit (Seoul, South Korea), an artificial intelligence (AI) powered medical image analysis software company, showcased the first live demonstration of the Lunit INSIGHT cloud-based AI solution for real-time image analysis at the 2017 Radiology Society of North America Annual Meeting (RSNA) held from November 26 through December 1 in Chicago.

Lunit develops advanced medical image analytics and novel imaging biomarkers via cutting-edge deep learning technology, in order to empower healthcare practitioners to make more accurate, consistent, and efficient clinical decisions. At this year’s RSNA, Lunit's exhibition booth was part of the "machine learning showcase," along with Google Cloud, NVIDIA, and other top exhibitors.

Image: Anthony S. Paek, CEO of Lunit, introduces Lunit INSIGHT (Photo courtesy of Lunit).
Image: Anthony S. Paek, CEO of Lunit, introduces Lunit INSIGHT (Photo courtesy of Lunit).

Lunit's AIs are trained using a huge collection of de-identified clinical data from its partner hospitals, and the company has directly used over 1 million well-curated high-quality case images in its research. Using the given image data, the AI algorithms are then specifically trained to detect target diseases or radiologic findings, including lung cancer, tuberculosis, pneumonia, pneumothorax, and breast cancer for chest X-ray and mammograms. Lunit's AI solutions have been proven to significantly increase the diagnostic performance of its users from non-radiology physicians to radiology experts by up to 20%.

The Lunit INSIGHT cloud-based imaging AI platform delivers its state-of-the-art AI models, with the chest X-ray solution being the first one to be unveiled. Lunit's chest X-ray solution detects major chest abnormalities, lung nodule/mass, consolidation, and pneumothorax, with an unprecedented high level of accuracy 一 97% standalone accuracy in nodule detection, 99% for consolidation and pneumothorax.

Lunit INSIGHT is currently available to the public, and users can upload their medical images online at the Lunit INSIGHT webpage. The AI analysis results appear in just a few seconds, including the level of abnormality as well as the visualization of the AI's attention map. Lunit's solutions will also be integrated into the systems of various companies including Nuance, EnvoyAI, and Infinitt Healthcare.

In addition to the chest X-ray solution, Lunit INSIGHT for Mammography for the detection of suspicious breast cancer lesions is in the final stages of development and is expected to be released for the public by the first quarter of 2018. The company is also developing similar solutions for digital breast tomosynthesis, chest CT, and coronary CT angiography.

"Lunit's vision is to develop advanced software for medical data analysis and interpretation that goes beyond the level of human vision," said Anthony S. Paek, CEO of Lunit. "In presenting Lunit INSIGHT, we hope to contribute in opening a new era of medical practice, by helping and empowering healthcare professionals to make more accurate, consistent, and efficient clinical decisions for the patients."

Related Links:
Lunit


Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
Silver Member
Wireless Mobile ECG Recorder
NR-1207-3/NR-1207-E
New
Compact C-Arm
Arcovis DRF-C S21

Latest AI News

AI-Powered Algorithm to Revolutionize Detection of Atrial Fibrillation

AI Diagnostic Tool Accurately Detects Valvular Disorders Often Missed by Doctors

New Model Predicts 10 Year Breast Cancer Risk