InferRead CT Lung

Infervision

Source: http://www.infervision.com/
InferRead CT Lung is a processing solution for lung cancer screening. It recognizes the core features of lung cancer, determine the characteristics of suspected lung nodules in different image sequences and aims to aid early-stage diagnosis. This solution provides information on nodules, including position, size, density, malignancy rate and evolution.
Product specifications Information source: Vendor
Last updated: March 31, 2020
General
Product name InferRead CT Lung
Company Infervision
Subspeciality Chest
Modality CT
Disease targeted lung cancer
Key-features lung nodule detection, report generation, multi-timepoint analysis
Suggested use During: interactive decision support (shows abnormalities/results only on demand), During: report suggestion, After: diagnosis verification
Data characteristics
Population
Input CT thorax
Input format DICOM
Output Type of lesions (solid, calcified, GGN nodules, semi-solid, etc.), location of each lesion (layer and anatomical location), density of the lesion, volume of the lesion, degree of malignancy of the lesion, draft report
Output format DICOM overlay, pdf file (draft report), DICOM GSPS, webviewer (description of lesion features)
Technology
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Stand-alone third party application, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 1 - 10 minutes
Certification
CE Certified, Class IIa
FDA No or not yet
Market presence
On market since 01-2020 (Europe), 2016 (China)
Distribution channels
Countries present (clinical, non-research use) 7
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Subscription
Based on Number of installations
Evidence
Peer reviewed papers on performance
Non-peer reviewed papers on performance
Other relevant papers