Vendor source

Reviewed 25 Sept 2026

Completeness Comprehensive (81%)

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

Supported by 15 papers

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Key Features

  • CAC-score
  • Emphysema score
  • Lung-RADS score
  • Nodule classification
  • Nodule detection
  • Nodule volume quantification
  • VDT

General Information

Product name

AVIEW LCS+

Modality

CT

Main task

Not specified

Subspeciality

Chest

Disease targeted

Lung Cancer, COPD, coronary artery calcifications

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIb

Verified by Health AI Register

FDA Certification

Pathway:

510(k) cleared

Class:

Class II

FDA Number:

Verified by Health AI Register

Other certifications

Not specified

Evidence & Research

Peer-Reviewed Papers

Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening

European Radiology · Aug 2026

Impact of CT slice thickness reduction algorithm on AI-based lung nodule detection in chest CTs of colorectal cancer patients

Quantitative Imaging in Medicine and Surgery · Aug 2026

Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence

European Radiology · May 2026

Negligible impact of perifissural nodules in an AI-first reader workflow from UK lung screening trial

European Radiology · Mar 2026

AI performance for nodule volume doubling time in the follow-up of the UKLS lung cancer screening study compared to expert consensus and histological validation

European Journal of Cancer · Nov 2025

Potential for AI as first reader in lung cancer screening

European Journal of Radiology · Nov 2025

Artificial intelligence as an independent reader of risk-dominant lung nodules: influence of CT reconstruction parameters

European Radiology · Aug 2025

Comparison of nodule volumetric classification by using two different nodule segmentation algorithms in an LDCT lung cancer baseline screening dataset

European Journal of Radiology · Jul 2025

Histological proven AI performance in the UKLS CT lung cancer screening study: Potential for workload reduction

European Journal of Cancer · Feb 2025

Feasibility of AI as first reader in the 4-IN-THE-LUNG-RUN lung cancer screening trial: impact on negative-misclassifications and clinical referral rate

European Journal of Cancer · Dec 2024

Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy

Diagnostic and Interventional Radiology · Sep 2024

Absolute ground truth-based validation of computer-aided nodule detection and volumetry in low-dose CT imaging

Physica Medica · Apr 2024

Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification

Lung Cancer · Jan 2022

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

European Radiology · Oct 2020

Implementation of the cloud-based computerized interpretation system in a nationwide lung cancer screening with low-dose CT: comparison with the conventional reading system

European Radiology · Aug 2020

Technical Specifications

Patient population age

Not specified

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

Copy-paste to report, Encapsulated DICOM PDF, PDF, SR

Integration

Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Stand-alone third party application, Stand-alone webbased

Deployment

Cloud-based, Hybrid solution, Locally on dedicated hardware, Locally virtualized (virtual machine, Docker)

Trigger for analysis

Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload

Processing time

1 - 10 minutes

Population

All adult non-enhanced chest CTs, including screening population

Market Presence

On market since

11-2017

AI Platforms

Alma Health Platform, Bayer Pharmaceuticals, Blackford Analysis, CARPL.AI, deepc, HCKonnect, Incepto, Philips Healthcare, Sectra, Terarecon, TeraRecon

Resellers

RMS Medical Devices

Countries present

10+

Paying clinical customers

Not specified

Research/test users

10+

Pricing Information

Pricing model

One-off payment, Subscription

Based on

Number of installations, Number of users

Source: Vendor | First published: May 2, 2024 | Last updated: Sep 25, 2026