LungPrint® Discovery is a fully-automatic, AI-powered analysis of a chest CT scan, empowering radiologists with crucial visual and quantitative information relevant to COPD (emphysema) and ILD (interstitial lung disease). It provides greater clinical value to pulmonary care teams while increasing the efficiency of radiologists. LungPrint Discovery delivers an early warning of lung density abnormalities that may be indicative of COPD or ILD.


Improved efficiency

Novel visualizations and fully-automated quantification of clinically significant areas of interest, help radiologists form a global impression of the scan quickly.

Quantitative tracking

Monitoring of disease progression takes the guesswork out of assessing change.

Clinical value

Empowers radiologists to deliver high-value quantitative results to clinicians, including clinically significant low and high density abnormalities, and functional volumes with lobar precision.

Early warning

Draws early attention to lung density abnormalities that may be indicative of COPD or ILD – both of which are diagnosed late in many cases.

  • Unique visualization
    Optimizes visualization of the airways in a spatially intuitive context via Hyperion View – a
    novel, patent-pending topographic multiplanar reformatted (tMPR) rendering.
  • Lobar density
    A visual and quantitative aid for lung density and volume assessment utilizing clinically
    validated surrogates that may be indicative of emphysema and interstitial lung disease.
  • Functional quantification
    Automatically quantifies the percentage of functional-density indicative of normal tissue
    defined by the % of voxels that fall between -950HU and -600HU.
  • Trachea analysis
    Provides a reliable tracheal analysis as a quantitative shape evaluation to quickly gauge the
    presence of tracheal abnormalities identifiable on inspiratory CT.

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“LungPrint Discovery is a game-changer for the reporting of thoracic CT scans. The new airway display is remarkable.”

Dr John Newell, Professor of Radiology, University of Iowa