Augmented diagnosis, quantification, characterization, image reconstruction: medical artificial intelligence at your fingertips for radiology

Veye Lung Nodules

AI solution for lung nodule detection and management on chest CT scans

Veye Lung Nodules supports radiologists in detecting and assessing pulmonary nodules on chest CTs. It automatically analyses hundreds of DICOM images scan to detecte, quantify, measure, classify and track the growth of pulmonary nodules. Veye Lung Nodules delivers its results in a fully integrated environment, within the usual radiology workflow and infrastructure.

Automatic detection and characterisation of pulmonary nodules

Veye Lung Nodules performs measurements in 2D and 3D settings, classifies solid and sub-solid nodules (including groundglass opacity) and has an adjustable detection threshold.

Lung nodule growth assessment if most recent prior available in the PACS

Veye Lung Nodules retrieves the most recent prior, if available, and automatically assesses the growth percentage and volume doubling time (VDT) for each nodule.

Automatic generation of a guideline-driven report and integration of an additional series directly into the PACS

Veye Reporting prepopulates a report with results from Veye Lung Nodules, following lung cancer screening protocols. The report contains a complete description of every nodule detected (diameter, volume, typology, VDT, ..) and a 3D rendering.

  • lung cancer
  • nodule
  • pulmonary
  • scanner
  • chest CT
  • detection
  • growth assessment

Save time

speed

Save time by fully automating cumbersome tasks such as nodule measurements and prior comparison

Improve accuracy

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Reduce the variability of intra- and inter-observer measurements and standardise patient follow-up

Integrate in your infrastructure

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Integrate an AI assistant tool into your existing and standard workflow and infrastructure

Save time, gain confidence in your diagnosis and streamline your workflow with Incepto

Testimonial

“Veye Lung Nodules helps us by automatically detecting the nodules, measuring them and comparing them to the previous images. It helps us spend more time with patients and practitioners, and do our jobs better.”

Dr Wouter de Monyé,

Spaarne Gasthuis,
Haarlem, the Netherlands

Publications

  1. Lung Nodule and Cancer Detection in CT Screening. Geoffrey D Rubin, MD. J Thorac Imaging. 2015 Mar; 30(2): 130–138.
  2. Med Image Anal. 2019 Jul;55:15-26. doi: 10.1016/j.media.2019.03.010. Epub 2019 Mar 28. Pulmonary nodule detection in CT scans with equivariant CNNs. Winkels M1, Cohen TS2.
  3. Martins Jarnalo CO, Linsen PVM, Blazís SP, van der Valk PHM, Dickerscheid DBM. Clinical evaluation of a deep-learning-based computer-aided detection system for the detection of pulmonary nodules in a large teaching hospital. Clin Radiol. 2021 Nov;76(11):838-845. doi: 10.1016/j.crad.2021.07.012. Epub 2021 Aug 14. PMID: 34404517.

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