Saige Lung (Veye Lung Nodules)

This AI assistant supports radiologists with detecting, classifying and tracking the growth of pulmonary nodules. Saige Lung (previously Veye Lung Nodules by Aidence) integrates into the PACS and is currently in use across Europe in both routine clinical practice and lung cancer screening programs.
Information source: Vendor
Last updated: Nov. 30, 2023

General Information

Product name Saige Lung (Veye Lung Nodules)
Company DeepHealth
Subspeciality Chest
Modality CT
Disease targeted Lung cancer
Key-features Nodule detection, nodule classification, volume quantification, growth calculation (prior study retrieval)
Suggested use During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion

Technical Specifications

Data characteristics
Input low or standard dose CT scans, maximum axial slice thickness of 3mm, non-contrast or post-contrast, multi-slice
Input format DICOM
Output Image annotations, grayscale presentation state (GSPS), burn-in series, table of quantified values
Output format DICOM, PDF
Integration Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
Trigger for analysis Automatically, right after the image acquisition
Processing time 1 - 10 minutes


Certified, Class IIb , MDR
FDA No or not yet
Intended Use Statements
Intended use (according to CE) Assist physicians in their review of CT scans in the detection, classification, quantification and growth assessment of solid and sub-solid pulmonary nodules using low-dose or standard-dose and non-contrast or post-contrast scans with a maximum axial slice thickness of ≤3mm. Veye lung Nodules is intended for use as a second or concurrent reader.


Market presence
On market since 12-2017
Distribution channels Nuance PIN, Incepto, Sectra, deepcOS, Blackford, Nordic Medtech, Inobe
Countries present (clinical, non-research use) 7
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing model Pay-per-use
Based on Number of scans analysed


Peer reviewed papers on performance

  • Higher agreement between readers with deep learning CAD software for reporting pulmonary nodules on CT (read)

  • Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population (read)

  • Clinical evaluation of a deep-learning-based computer-aided detection system for the detection of pulmonary nodules in a large teaching hospital (read)

  • Effect of CT reconstruction settings on the performance of a deep learning based lung nodule CAD system (read)

Non-peer reviewed papers on performance
Other relevant papers