Products
Companies
News
About
About
Add your product
Contact
Newsletter
×
Subscribe to our monthly newsletter
Subscribe
Products
AVIEW LCS+
AVIEW LCS+
Coreline Soft
The LCS+ software from Coreline Soft provides information on the 3 main lung diseases: lung cancer, COPD and coronary artery calcification based on a Low Dose, non-enhanced, non-gated CT-scan
Information source:
Vendor
Last updated:
Jan. 21, 2022
General Information
Technical Specifications
Regulatory
Market
Evidence
General Information
General
Product name
AVIEW LCS+
Company
Coreline Soft
Subspeciality
Chest
Modality
CT
Disease targeted
Lung Cancer, COPD, coronary artery calcifications
Key-features
Nodule detection, nodule classification, nodule volume quantification, VDT, Lung-RADS score, Emphysema score, CAC-score
Suggested use
During: perception aid (prompting all abnormalities/results/heatmaps), report suggestion
Technical Specifications
Data characteristics
Population
All adult non-enhanced chest CTs, including screening population
Input
Chest CT of different vendors. Non-enhanced, non-gated
Input format
DICOM
Output
Emphysema index (LAA), Coronary calcification score per branch, CAD, Lung-RADS, VDT, solid/non-solid
Output format
SR, PDF, copy-paste to report, Encapsulated DICOM PDF
Technology
Integration
Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Stand-alone third party application, Stand-alone webbased
Deployment
Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based, Hybrid solution
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
Regulatory
Certification
CE
Certified, Class IIa
, MDD
FDA
510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE)
AVIEW LCS PLUS is intended for the review and analysis and reporting of thoracic CT images for the purpose of characterizing nodules in the lung in a single study, or over the time course of several thoracic studies. Characterizations include nodule type, location of the nodule and measurements such as size (major axis, minor axis), estimated effective diameter from the volume of the nodule, the volume of the nodule, Mean HU (the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass (mass calculated from the CT pixel value), and volumetric measures (Solid Major; length of the longest diameter measured in 3D for a solid portion of the nodule. Solid 2nd Major: The length of the longest diameter of the solid part, measured in sections perpendicular to the Major axis of the solid portion of the nodule), VDT (Volume doubling time), Lung-RADS (classification proposed to aid with findings) and CAC score and LAA analysis. The system automatically performs the measurement, allowing lung nodules and measurements to be displayed.
Market
Market presence
On market since
11-2017
Distribution channels
RMS Medical Devices, Alma AI MARKETPLACE, Eureka Clinical AI
Countries present (clinical, non-research use)
10+
Paying clinical customers (institutes)
Research/test users (institutes)
10+
Pricing
Pricing model
Subscription, One-off payment
Based on
Number of users, Number of installations
Evidence
Evidence
Peer reviewed papers on performance
Absolute ground truth-based validation of computer-aided nodule detection and volumetry in low-dose CT imaging
(read)
Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification
(read)
Variability in interpretation of low-dose chest CT using computerized assessment in a nationwide lung cancer screening program: comparison of prospective reading at individual institutions and retrospective central reading
(read)
Implementation of the cloud-based computerized interpretation system in a nationwide lung cancer screening with low-dose CT: comparison with the conventional reading system
(read)
Non-peer reviewed papers on performance
Other relevant papers