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LunitINSIGHT MMG

AI solution for Mammography

Online Demo

  • 96%

    Detects breast cancer
    with 96-99%
    accuracy

  • 240,000

    Trained with a large-scale (more than 50,000 breast cancer cases), high-quality (biopsy-proven cases) training set.

  • 50%

    Reduces the chances of physicians
    overlooking breast cancer
    in the screening mammography by 50%

  • 2797

    Currently approved for
    commercial sales
    in Europe and Korea.

  • Background
  • Product Description
  • Major Benefits
  • Example Cases
BackgroundProduct DescriptionMajor BenefitsExample Cases
Online Demo

Background

What if breast cancer can be found in
the early stages?

  • Improving mammography interpretation

    Lunit INSIGHT in Yonsei Severance Hospital, Yongin, Korea

  • Reducing the chances of missing breast cancer

  • 40% of breast cancer patients can be
    diagnosed earlier

    Detected

    Lunit AI score
    71.2%

    Missed

    Breast Cancer
    Diagnosed

    Detected

    Lunit AI score
    91.8%

    Missed

    Breast Cancer
    Diagnosed

    Detected

    Lunit AI score
    99.9%

    Breast Cancer
    Diagnosed

  • When Detected
    Early by AI

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

Product Description

Lunit INSIGHT MMG is
all about breast cancer.

  • Accurately
    detects lesions suspicious of
    breast cancer in
    a mammogram.

    Developed using Lunit’s cutting-edge
    deep learning technology

  • Generated by
    Lunit INSIGHT MMG

    • Detected Location

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

    • Abnormality Score

      The AI generates an abnormality score for each side of the breast, which reflects the AI’s calculation of the actual presence of the detected breast cancer.

    • Density Assessment

      The AI generates its assessment of breast density, categorized into four types.

Major Benefits

Reduce the risk of overlooking
breast cancer by 50%

  • Detect more
    breast cancers

    The combination of first-reader radiologists and Lunit AI detects more breast cancers, than not only the first-reader and second-reader radiologists but also the double reading by radiologists.¹

  • Fast triage of
    normal cases

    Triage up to 60% of the entire cases without human interpretation; reduce workload by more than half in mammogram interpretation.2

  • Improved reading performance of
    general radiologists

    General radiologists can improve their reading performance up to a breast-specialist-level.3

  • Early diagnosis of
    breast cancer

    Detect T1 and node-negative breast cancer with 91% and 87% accuracy, respectively.4

  • Support for decision-making on BI-RADS
    3 and 4 cases

    For difficult cases, compare with the AI results and decide with confidence for additional exams such as ultrasound and biopsy.

  • Improved diagnostic accuracy for
    dense breasts

    Improve diagnostic accuracy for dense and fatty breasts by up to 9% and 22%, respectively.5

Example Cases

Breast cancer
can be tricky to find.
Not with AI.

  • A mass in the right breast, diagnosed as invasive ductal carcinoma, is shown to be properly detected with a malignancy score of 98%.

    detected with a
    malignancy score of

    98%

    A lesion with microcalcifications in the right breast, diagnosed as ductal carcinoma in situ, is shown to be properly detected with a malignancy score of 58%.

    detected with a
    malignancy score of

    58%

  • Distributed in
    partnership with

  • Major Customers

Publications featuring
Lunit INSIGHT MMG

Breast

Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study

Mattie Salim, MD et al.

Lancet Digital Health (2020)

Breast

External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms

Mattie Salim, MD et al.

JAMA Oncology (2020)

Breast

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Thomas Schaffter, PhD et al.

JAMA Network Open (2020)

Breast

Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study

Hyo-Eun Kim, PhD et al.

Lancet Digital Health (2020)

Breast

Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study

Eun-Kyung Kim et al.

Scientific Reports (2018)

Our solutions

Watch and learn more about
Lunit INSIGHT MMG

Lunit YouTube

Reference

  • Background

    1Myers ER, Moorman P, Gierisch JM, et al. Benefits and harms of breast cancer screening: a systematic review. JAMA 2015;314:1615-34.

    2Thurfjell EL, Lernevall KA, Taube A. Benefit of independent double reading in a population-based mammography screening program. Radiology 1994;191:241-4.

    3Yankaskas BC, Klabunde CN, Ancelle-Park R, et al. International comparison of performance measures for screening mammography: can it be done? J Med Screen 2004;11:187-93.

    4Ciatto S, Ambrogetti D, Risso G, et al. The role of arbitration of discordant reports at double reading of screening mammograms. J Med Screen 2005;12:125-7.

    5http://breastscreening.cancer.gov.

  • Major Benefits

    1Mattie Salim, Erik Wåhlin, Karin Dembrower, et al., External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms, JAMA Oncology, 2020

    2Karin Dembrower, Erik Wåhlin, et al., Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study, THE LANCET Digital Health 2020

    3 4 5Hyo-Eun Kim, Hak Hee Kim, et al., Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study, THE LANCET Digital Health 2020