Lunit INSIGHT MMG

Lunit

Lunit INSIGHT MMG is deep learning based software that assists radiologists in the interpretation of mammograms. The AI solution automatically detects suspicious areas for breast cancer on mammograms including mass, calcification, distortion and asymmetry. The analysis result contains (1) localization of suspicious areas in color or outline and (2) abnormality scores reflecting the probability that the detected area is malignant.
Product specifications Information source: Vendor
Last updated: July 13, 2020
General
Product name Lunit INSIGHT MMG
Company Lunit
Subspeciality Breast
Modality Mammography
Disease targeted Breast cancer
Key-features breast cancer detection, abnormality score
Suggested use Before: stratifying reading process (non, single, double read), adapting worklist order
During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion
After: diagnosis verification
Data characteristics
Population Female aged 19 years or older, screening population
Input Full-field digital mammogram, Synthesized 2D mammogram
Input format DICOM
Output localization(Color Map or Grayscale Map), abnormality score for each lesion/side, binary assessment of abnormality, worklist order
Output format DICOM Secondary Capture, DICOM GSPS(Grayscale Softcopy Presentation State)
Technology
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 3 - 10 seconds
Certification
CE Certified, Class IIa
FDA No or not yet
Market presence
On market since 06-2020
Distribution channels various
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model
Based on
Evidence
Peer reviewed papers on performance

  • Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study (read)

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

Non-peer reviewed papers on performance
Other relevant papers H Nam, et al. Data-Driven Imaging Biomarker for Breast Cancer Screening in Mammography: Prediction of Tumor Invasiveness in Mammography. RSNA. 2019
S Lee, et al. Diagnostic Performances of Artificial Intelligence (AI)-based Diagnostic Support Software for Mammography: Results Using a Standardized Test Set Built for External Validation. RSNA. 2019
HE Kim, et al. Data-driven Imaging Biomarker for Breast Cancer Screening in Mammography: Early Detection of Breast Cancer. RSNA. 2019
HJ Lee, et al. Data-driven Imaging Biomarker for Breast Cancer Screening in Digital Breast Tomosynthesis: Multi-domain Learning with Mammography. RSNA. 2019
HE Kim, et al. Increase of Cancer Detection Rate and Reduction of False-Positive Recall in Screening Mammography using Artificial Intelligence: A Multi-Center Reader Study. RSNA. 2019
EK Kim, et al. Data­-driven Imaging Biomarker for Breast Cancer Screening in Mammography ­Reader Study. RSNA. 2018
S Kim, et al. Data­-Driven Imaging Biomarker for Breast Cancer Screening in Digital Breast Tomosynthesis. RSNA. 2018
EK Kim, et al. Advanced Data-Driven Imaging Biomarker for Breast Cancer Screening in Mammography. RSNA. 2017
EK Kim, et al. Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening. RSNA. 2016