Lunit INSIGHT DBT

AI-powered solution that helps in the interpretation digital breast tomosynthesis images.

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Overview

Lunit INSIGHT DBT is an AI solution that identifies and classifies suspected areas for breast cancer on tomosynthesis images. The solution automatically analyzes DICOM images and provides visualization and quantitative estimation of the presence of malignant lesions.

Lunit's AI Engine is trained on DBT Datasets from the United States and South Korea and comprises of images acquired from Hologic and GE devices. This makes INSIGHT DBT suitable for the analysis of mammograms of various breast densities, ethnicities and manufacturers.

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Features

  • Provides location of suspicious lesions.
  • Quantifies the likelihood of the presence of malignancy for each suspicious lesion through abnormality score in a range of 0-100.
  • Characterizes a breast cancer finding as Mass, Calcification or Mass with Calcification. Soft tissue lesion (mass) includes mass, asymmetry and architectural distortion.
  • Displays analysis result in DICOM SR, DICOM SC, and GSPS.
  • Enables users to jump to the best visible slice of the suspicious lesion in one click.

Benefits

A study found that using Lunit DBT resulted in:¹

  • Improvement in radiologists’ diagnostic performance.
  • Decrease in reading times and increased reporting efficiency by using AI as a reading support tool.
  • Better performance for both fatty and dense breasts.
  • Increase reporting efficiency by using AI as a reading support tool.
  • Increased inter reader agreement with AI.

Image examples

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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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¹Eun Kyung Park, Kwak S, Lee W, Joon Suk Choi, Kooi T, Kim EK. Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time. Radiology Artificial intelligence. 2024 Apr 3;‌