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AI analysis for Chest x-ray
AI analysis for Mammography
AI analysis for Tissue Slides
Screening mammography is the only single modality proven to improve breast cancer survival, and is the main test for breast cancer screening.1 However, screening breast cancer by mammography is far from ideal. False negative interpretations have been reported to range from 10 to 30%,2-4 and among the average 10% of subjects recalled for follow-up on initial screening mammogram, less than 5% are eventually diagnosed with cancer, representing a 95% false positive rate.5
Developed using Lunit’s cutting-edge deep learning technology,6 Lunit INSIGHT MMG accurately detects lesions suspicious of breast cancer in the form of diagnostic support tool. The AI solution generates (1) location information of detected lesions in the form of heatmaps and (2) abnormality scores reflecting the probability that the detected lesion is malignant. The solution is indicated to be directly involved in the primary interpretation process of radiologists.
Primary value proposition
- Assist both breast specialists and general radiologists (non-specialists) to increase cancer detection rate and decrease recall rate when interpreting screening mammograms.
- Enable general radiologists to perform at specialist level.
Training & Validation
- Trained with a large-scale (>200,000 total cases, >50,000 cancer cases), high-quality (biopsy-proven cases) training set.
- Demonstrated to perform at a standalone accuracy of 96% in ROC AUC.7
- Clinically validated to significantly improve the interpretive capabilities of radiologists upto 10%.8
- Currently in preparation for regulatory approval in various markets worldwide, including FDA, CE, MFDS.
CASE #1. A mass in the right breast, diagnosed as invasive ductal carcinoma, is shown to be properly detected with a malignancy score of 98%.
CASE #2. 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%.