InferRead Lung CT.AI utilizes deep learning technologies to automatically identify lung nodules and perform nodule quantifications accurately and efficiently. Developed by Infervision, and validated through FDA clinical trials and retrospective, multi-reader multi-case (MRMC) protocols, the tool has produced results that show it can reduce exam reading times up to 30% and improve discovery of missed nodules by up to 35%.

Benefits

Proven.

FDA-cleared and validated on more than 500K exams, already in use at more than 400 sites worldwide.

Fast.

Rapid detection of nodule location and measurements reduce exam reading times up to 30%.

Accurate.

Accurate identification of various types of nodules (solid, semi-solid, GGN) improves discovery of missed nodules by up to 35%.

Flexible.

Fully configurable to your workflow, scanner and infrastructure agnostic.

Features

InferREAD Lung CT.AI features

  • Measure
    Identify nodule location and measure slice #, max/average diameter, and volume.
  • Predict
    Nodule malignancy prediction based on deep learning and radiomic features.
  • Compare
    Primary and prior-images comparison function for nodule tracking.
  • Report
    Automatically generate customizable result reports

Testimonials

“Throughput has been increased, delivering a reduction in cost and a resource savings. Decrease in the peak interpretation time from 6-7 min to 4-5 min after implementing InferRead Lung CT.AI.”

Dr. Matthew Hoimes, MD, MS Wake Radiology | UNC Rex Healthcare

“The tremendous potential for lung cancer screening to reduce mortality in the US is very much unrealized due to a combination of reasons. Based on our experience reviewing the algorithm for the past several months and my observations of its extensive use and testing in China, I believe that Infervision's InferRead Lung CT.AI application can serve as a robust lung nodule “spell-checker” with the potential to improve diagnostic accuracy, reduce reading times, and integrate with the image review workflow”

Eliot Siegel, MD, Professor and Vice-Chair of research information systems in radiology, University of Maryland School of Medicine.

“Infervision has been an excellent AI partner to Covera Health thus far. Not only do their tools work phenomenally well, but they’re also very flexible when it comes to implementation and customer-specific goals - something we encountered first-hand. Would highly recommend working with them.”

Daniel Myers, CFO, Covera Health