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: Nov. 9, 2022

General
Product name	Lunit INSIGHT MMG
Company	Lunit
Subspeciality	Breast
Modality	Mammography
Disease targeted	Breast cancer
Key-features	Breast cancer detection, abnormality score, density assessment
Suggested use	Before: adapting worklist order, flagging acute findings
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, grayscale map, combined map, single color map), abnormality score for each lesion/side, binary assessment of abnormality, worklist order, density assessment (A,B,C and D also 1~10 score)
Output format	DICOM Secondary Capture, DICOM GSPS(Grayscale Softcopy Presentation State), DICOM SR (Structured Report), HL7
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 , MDR
FDA	510(k) cleared, Class II
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) Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study (read) External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms (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