LunitINSIGHT CXR

AI solution for Chest x-ray

Online Demo
  • 97-99%

    Detects with 97-99% accuracy.
    Accurately detects 10 of the most
    common findings in a chest x-ray.
    *Supports Tuberculosis Screening

  • 3,500,000

    Trained with a large-scale,
    high-quality (clinically/CT-proven cases)
    training set

  • Currently approved for commercial sales
    in Europe and other markets such as
    Australia, Brazil, Thailand, Korea, and etc.

  • Background
  • Product Description
  • Major Benefits
  • Example Cases
Online Demo

Background

What if the most basic test can catch
the least visible findings?

  • Improving the most basic and universal diagnostic test

  • Alleviating
    the burden
    in radiology workflow

  • 50% of lung cancer patients can be diagnosed earlier

    Detected

    Lunit AI score
    16.7%

    Missed

    Lung Cancer
    Diagnosed

    Detected

    Lunit AI score
    43.1%

    Missed

    Lung Cancer
    Diagnosed

    Detected

    Lunit AI score
    90.7%

    Lung Cancer
    Diagnosed

  • When Detected
    Early by AI

    *5-year survival
    *Reference: AJCC 8th Edition

Product Description

Lunit INSIGHT CXR covers
the vast majority of findings.

  • Accurately detects 10 of the most common findings in
    a chest x-ray.

    Developed by using Lunit’s cutting-edge
    deep learning technology.

    *Supports Tuberculosis Screening

  • Generated by
    Lunit INSIGHT CXR

    • Detected Location

      The AI generates the location information of detected lesions in the form of heatmaps and outlines.

    • Abnormality Score

      The AI generates an abnormality score which reflects the AI’s calculation of the actual presence of the detected lesion.

    • AI Report

      The AI provides a “case report” that summarizes the overall analysis result, narrowed down to each finding.

Major Benefits

Accurate and efficient diagnosis
boosted with AI

  • Lunit INSIGHT in Yonsei Severance Hospital,
    Yongin, Korea

  • Fast triage of
    normal cases

    Triage normal cases quickly and focus on reading abnormal cases

  • Efficient reading via
    exam prioritization

    Prioritize cases according to each abnormality scores, reducing reading time by 65% for normal cases and 25% for abnormal cases.1

  • Improved reading performance

    Non-radiology physicians, general radiologists, and even thoracic radiologists can improve their diagnostic accuracy for major chest abnormalities.2 3 4 5 6 7 8

  • Early diagnosis of
    lung cancer

    Reduce false negative cases and detect lung cancer at early stages with AI-aided detection of small, subtle pulmonary nodules.9

  • Streamlined workflow in Emergency Department

    Reduce reading time by 39%, with accelerated decision-making process and treatment.10

Example Cases

Never miss a finding.

  • A nodule, diagnosed as lung cancer, hidden behind the heart is properly detected, with an abnormality score of 44%. This case was missed by 8 out of 15 radiologists.

    Abnormality Score

    44%

    Radiologists Missed

    8 out of 15

    A nodule, diagnosed as lung cancer, in the right upper lung field is properly detected, with an abnormality score of 66%. This case was missed by 9 out of 9 radiologists.

    Abnormality Score

    66%

    Radiologists Missed

    9 out of 9

    A nodule, diagnosed as lung cancer, hidden behind the diaphragm is properly detected, with an abnormality score of 96%. This case was missed by 5 out of 9 radiologists.

    Abnormality Score

    96%

    Radiologists Missed

    5 out of 9

  • Distributed in
    partnership with

  • Major Customers

Publications featuring
Lunit INSIGHT CXR

Our solutions

Watch and learn more about
Lunit INSIGHT CXR

Lunit YouTube

Reference

  • Background

    1Radiation UNSCotEoA. Sources and effects of ionizing radiation: sources: United Nations Publications; 2000.

    2Quekel LG, Kessels AG, Goei R, van Engelshoven JM. Miss rate of lung cancer on the chest radiograph in clinical practice. Chest 1999;115:720-4.

    3Forrest JV, Friedman PJ. Radiologic errors in patients with lung cancer. Wes J Med 1981;134:485.

    4Kesselman A, Soroosh G, Mollura DJ, et al. 2015 RAD-AID Conference on International Radiology for Developing Countries: the evolving global radiology landscape. J Am Coll Radiol 2016;13:1139-44.

    5Monnier-Cholley L, Carrat F, Cholley BP, Tubiana J-M, Arrivé L. Detection of lung cancer on radiographs: receiver operating characteristic analyses of radiologists’, pulmonologists’, and anesthesiologists’ performance. Radiology 2004;233:799-805.

    6Eng J, Mysko WK, Weller GE, et al. Interpretation of emergency department radiographs: a comparison of emergency medicine physicians with radiologists, residents with faculty, and film with digital display. Am J Roentgenol 2000;175:1233-8.

    7Potchen EJ, Cooper TG, Sierra AE, et al. Measuring performance in chest radiography. Radiology 2000;217:456-9.

    8Milner RC, Culpan G, Snaith B.Radiographer reporting in the UK: is the current scope of practice limiting plain-film reporting capacity? Br J Radiol. 2016 Sep 89(1065):20160228. doi:

  • Major Benefits

    1Internal test results

    2Ju Gang Nam, Sunggyun Park, et al., Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs, Radiology, 2018

    3Eui Jin Hwang, Sunggyun Park, et al., Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs, Clinical Infectious Diseases, 2018

    4Eui Jin Hwang, Sunggyun Park, Kwang-Nam Jin, et al., Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs, JAMA Network Open, 2019

    5Jong Hyuk Lee, Sunggyun Park, et al., Deep learning–based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals, European Radiology, 2020

    6Eui Jin Hwang, Jung Hee Hong, et al., Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study, European Radiology, 2020

    7Jong Hyuk Lee, Hye Young Sun, et al., Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population, Radiology, 2020

    8Hyunsuk Yoo, Ki Hwan Kim, et al., Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs, JAMA Network Open. 2020

    9Sowon Jang, Hwayoung Song, et al., Deep Learning–based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs, Radiology, 2020

    10Jae Hyun Kim, Jin Young Kim, et al., Clinical Validation of a Deep Learning Algorithm for Detection of Pneumonia on Chest Radiographs in Emergency Department Patients with Acute Febrile Respiratory Illness, Journal of Clinical Medicine, 2020