Lunit INSIGHT MMG accurately detects lesions suspicious of breast cancer in a mammogram.
Internal Validation : (version) v.1.1.7.3 / (date) May 26, 2021 / (dataset) 180K / ROC AUC) 96%
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Features
According to the study published in THE LANCET Digital Health
Radiologists can improve diagnostic accuracy for dense and fatty breasts by up to 5% and 12%, respectively. ²
Dense breast cancer diagnosis increased by 5% with AI
Fatty breast cancer diagnosis increased by 12% with AI
2. 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 JAMA Oncology, Lunit INSIGHT MMG showed the best performance in detecting malignant lesions among 3 AI solutions. ¹
Figure. Receiver Operating Characteristic Curves for the1. Mattie Salim, Erik Wåhlin, Karin Dembrower, et al. External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms.
Lunit INSIGHT MMG automatically analyzes and generates quantitative density assessment* during breast screening.
* Not available in the U.S.ALunit INSIGHT MMG successfully analyzed the mammogram of an overlooked case in 2020, where cancer was missed and the patient was diagnosed 2 years later in 2022.
According to a study published in THE LANCET Digital Health
Among the women with
top AI scores for negative mammograms after double reading, 13% more cancers were detected earlier with Lunit INSIGHT MMG.²
3. 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 LANCET Digital Health. 2020
According to a study published in THE LANCET Digital Health,
Lunit INSIGHT MMG can triage 60% of the entire cases without missing any breast cancer.³
The suggested workflow model, of which the AI score functions as supportive information, reduces radiologists’ reading volume and complements their interpretations.
3. 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.
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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