Lunit INSIGHT MMG

Breast cancer diagnosis solution that assists physicians in the interpretation of mammograms.

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Overview

10 to 30% of breast cancers are still missed when reading mammograms¹ to this day. Lunit INSIGHT MMG aims to improve readers’ performance and ensure early breast cancer detection.

Lunit INSIGHT MMG is a computer-assisted Detection/Diagnosis (CADe/x) software that identifies and classifies suspicious areas for breast cancer on mammograms via deep learning technology. It was trained with a large-scale, high-quality (clinically/CT-proven cases) training dataset of 240,000 cases.

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Features

Lunit INSIGHT MMG generates:
• Location of breast cancer in the form of heatmaps and/or contour maps.
• Abnormality Score reflecting the probability of the presence of breast cancer.
• An assessment of breast density (not FDA-cleared).
• Has regulatory clearance in USA, Canada, EU, UK, Australia and New Zealand.

Benefits

  • Expected to improve diagnostic accuracy for dense and fatty breasts.
  • In a study, a hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% reduction in the interval/missed cancer rate.²
  • Supports early diagnosis of breast cancer, including interval cancer and next-round screen-detected cancer.²
  • Proven potential to replace one of the readers in a double-reading breast cancer screening environment, while improving cancer detection rate and reducing recall rates.³

Image examples

MMG-8
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Contour maps

Heat maps

To find out more about this solution or any of the other 140+ applications on the Blackford Platform, please book a discovery call with our team.

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We’d welcome the opportunity to learn more about your AI needs and to explain how partnering with Blackford can drive efficiency and provide ongoing value.

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¹Majid AS, de Paredes ES, Doherty RD, Sharma NR, Salvador X. Missed breast carcinoma: pitfalls and pearls. Radiographics: A Review Publication of the Radiological Society of North America, Inc [Internet]. 2003 Jul 1;23(4):881–95. ‌

²Mustafa Ege Seker, Yilmaz Onat Koyluoglu, Ayse Nilufer Ozaydin, Sibel Ozkan Gurdal, Beyza Ozcinar, Neslihan Cabioglu, et al. Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program. European Radiology. 2024 Feb 22;
 
³Dembrower K, Crippa A, Colón E, Eklund M, Strand F. Artificial Intelligence for Breast Cancer Detection in Screening Mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority Study. The Lancet Digital Health. 2023 Sep 1;5(10).
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