AI Solution for Mammography
Less Doubts, More Confidence
96% AUC
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%
Global Partnership
Lunit INSIGHT MMG
User Testimonial
Features
Improved Diagnostic Accuracy, Especially For Dense Breast
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 12% with AI
12 %
Fatty breast cancer diagnosis increased by 5% with AI
5 %
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 of detecting malignant lesions among 3 AI solutions. ¹
Figure. Receiver Operating Characteristic Curves for the3 Artificial Intelligence Computer-Aided Detection Algorithms
1. Mattie Salim, Erik Wåhlin, Karin Dembrower, et al. External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms.
Density Assessment
Lunit INSIGHT MMG automatically analyzes and generates quantitative density assessment* during breast screening.
* Not available in U.S.A
40% Of Breast Cancer Patients Can Be Diagnosed Earlier
Lunit INSIGHT MMG successfully analyzed the mammogram of an overlooked case in 2020, where cancer was missed and the female patient was diagnosed 2 years later in 2022.
- 2018
- 2020
- 2022
Enable Early Diagnosis of Breast Cancer
13 %
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
Workflow Efficiency
60 %According to a study published in THE LANCET Digital Health
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.
Publication
- Mattie Salim, Erik Wåhlin, Karin Dembrower, et al. External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms. JAMA Oncology. 2020
- 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. THE LANCET Digital Health. 2020
- 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
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Publications featuring
The publications listed below are not subject to the review of medical device advertising and are intended to showcase Lunit’s technology.
Learn more about Lunit INSIGHT MMG
What do the medical journals say about AI-powered mammography?
Watch the animated video about Lunit INSIGHT MMG featured in peer-reviewed journals