
| Company Name | Coreline Soft |
| Founded | 2012 |
| Head office | Seoul, Korea |
| Website | https://www.corelinesoft.com/ |
| Contact | global.mkt@corelinesoft.com |
Lung-RADS reporting, Nodule classification, Nodule detection, Nodule measurement
It detects and quantifies pulmonary nodules, providing size, volume, nodule type, location, calcification, and spiculation. An automatic report based on the calculated Lung-RADS category is produced to assist in managing pulmonary nodules. Its follow-up registration and nodule matching aid the comparison of serial CT scans. Operation settings can be customized between sensitivity-oriented for high-risk patients and specificity-oriented for efficient screening.
* Formerly VUNO Med–LungCT AI from VUNO; recently transferred to Coreline Soft.
CAC-score, Emphysema score, Lung-RADS score, Nodule classification, Nodule detection, Nodule volume quantification, VDT
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
Air Trapping, Airway Measurements, Emphysema, Fissure Integrity, Low Attenuation Analysis (LAA), Lung lobe segmentation, Pulmonary Vessels., Vessel Analysis
AVIEW COPD gives COPD information based on a non-enhanced CT-scan of the thorax. Evaluation includes segmentation of lungs and lung lobes, low attenuation analysis, emphysema, vessel analysis, fissure integrity, air trapping, and airway measurements.
Agatston, Arterial age, Automatic segmentation, CAC score per branch, Mass score, Volume
Cononary Artery Calcium Scoring
And distribution, Classification, Fibrotic area localization, Lung and lobe segmentation, Pneumonia score
Lung Texture defines the fibrotic area of the lungs in 6 patterns and automatically generates a report by analyzing the location and distribution. Quantitative analysis help with difficult reading cases and reduce deviations from different reading techniques for diagnosis.
Autosegmentation of OAR
aview:RT ACS conducts automatic segmentation of normal organs using deep learning AI technology