Lunit to Present 5 Abstracts at the ECR Meeting 2022

Lunit has announced the presentation of five abstracts featuring its AI solutions for chest and breast radiology at the European Congress of Radiology (ECR) Meeting 2022, held in Vienna, Austria, July 13th through 17th.

  • Lunit to present five abstracts highlighting the practical effectiveness of Lunit AI suite in precision diagnostic practices
  • Lunit’s AI imaging biomarker leads to better prediction of breast cancer risk
  • Lunit INSIGHT CXR demonstrates its ability as part of a prognosis and intervention prediction model

SEOUL, South Korea, July 11, 2022 /PRNewswire/ --Lunit Inc. has announced the presentation of five abstracts featuring its AI solutions for chest and breast radiology at the European Congress of Radiology (ECR) Meeting 2022, held in Vienna, Austria, July 13th through 17th.

The ECR, along with the Radiological Society of North America (RSNA), is the world’s largest radiology congress, attended by renowned radiologists and medical device giants around the globe.

One of Lunit’s presentations will focus on the company’s AI-powered imaging biomarker for breast cancer risk assessment in mammography. The study demonstrates the AI-powered Imaging Biomarker in Mammography (IBM), developed by Lunit, making it possible to precisely forecast the risk of breast cancer for the next 5 years with a C-index of 0.684.1

Two joint studies by Lunit and the University of Cambridge explore the possibility of Lunit INSIGHT MMG, an FDA-cleared and CE-marked breast cancer detection AI product, being used to improve the UK breast screening workflow. The studies showed that Lunit INSIGHT MMG could play a role in the double reading screening settings and in particular, could help to increase the detection of interval cancers, which are detected between routine screening cycles.

Furthermore, a joint study with the University of Basel focuses on Lunit’s CE-marked chest x-ray AI solution, Lunit INSIGHT CXR. According to the study, the software analyzed 150 chest x-ray images with 97.3% accuracy and reduced the average reading time from 23.2 seconds to 14.1 seconds. The findings indicate that radiologists can substantially improve accuracy and productivity with the assistance of Lunit INSIGHT CXR.

Another major study that used medical data from nine COVID-19 treatment centers in Korea revealed the clinical efficacy of utilizing Lunit INSIGHT CXR as part of the prognosis and intervention prediction model for patients with COVID-19. When Lunit INSIGHT CXR was combined with other clinical modes and data, the model showed significantly improved prediction capabilities for patients’ prognosis (e.g., ICU admission, in-hospital mortality) and required medical interventions (e.g., O2 supplementation, mechanical ventilation, use of ECMO).

“Through the studies, Lunit has demonstrated the credibility of our AI-powered diagnostics solutions as well as how AI can make a significant difference in various medical practices,” said Brandon Suh, CEO of Lunit. “Based on this achievement, we intend to expand the partnership with global healthcare companies and medical institutions, increasing our market share worldwide.”

Lunit Booth

  • Visit us at EXPO X1 - AI area - Booth #30 for product demonstrations of Lunit INSIGHT CXR, Lunit INSIGHT MMG, and Lunit INSIGHT DBT.
    *Some of the products will be available for demonstration also at our global partners’ booths: FUJIFILM, Philips, INFINITT Healthcare, SECTRA and AGFA.
  • Available from July 13 to 17, 09:00 - 17:00 CEST

CEO Presentation at the AI Theatre

  • Brandon Suh, CEO of Lunit, will be on stage at the AI Theatre to give an industry presentation about the recent and upcoming product developments, and business activities on how our AI is clinically applied across global medical sites.
  • Title: “Conquer Cancer through AI: Precision Diagnostics in Chest and Breast Radiology”
    When: Wednesday, July 13, 13:25 - 13:31 CEST

Book your meeting with Lunit at ECR 2022 here.

1 C-index: The concordance index is one of the most used performance measures of survival models. It is the probability of concordance between the predicted and the observed survival. C-index 1 indicates perfect prediction accuracy and 0.5 is as good as a random predictor.

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