Lunit INSIGHT MMG accurately detects lesions suspicious of breast cancer in a mammogram.
Lunit INSIGHT MMG automatically analyzes and generates quantitative density assessment* during breast screening.* Not available in the U.S.A
Users can choose two options for breast density: 'Case Level' and 'Side Level'
Case Level : Indicates the average density of both breasts
Side Level : Indicates the density of each breast
Recently, the European Society of Breast Imaging (EUSOBI) released its new recommendations that patients should be informed of their breast density when screened.
According to a paper published in JAMA Oncology, Lunit INSIGHT MMG showed outstanding performance for breast cancer detection.Figure. Receiver Operating Characteristic Curves for the
Mattie Salim, Erik Wåhlin, Karin Dembrower, et al. External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms.
According to a paper published in THE LANCET Digital Health,
the accuracy of cancer diagnosis improved by 12% when Lunit INSIGHT MMG assisted in reading dense breast cases.
In addition, it has been reported that the accuracy of fatty breast cancer diagnosis improved by 5% when Lunit INSIGHT MMG assisted in reading mammograms.
Dense breast cancer diagnosis assisted by AI increased by 12%12 %
Fatty breast cancer diagnosis assisted by AI increased by 5%5 %
Hyo-Eun Kim, Hak Hee Kim, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study.
According to a study published in THE LANCET Digital Health,
it is possible to triage 60% of all cases as normal without missing any breast cancer.
The suggested workflow model, of which the AI score functions as supportive information, reduces radiologists’ reading volume and complements their interpretations.
Diagnosis of exclusion ￚ 60% of the entire cases were triaged into the exclusion category, which can be interpreted as negative cases in screening tests.
Karin 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 algorithm is updated annually with additional training datasets or new features by the manufacturer to manage the performance.
As this product is a medical device, please read the user manual carefully before use.
The publications listed below are not subject to the review of medical device advertising and are intended to showcase Lunit’s technology.
Watch the animated video about Lunit INSIGHT MMG featured in peer-reviewed journals