LuCAS-plus can automatically detect and suggest nodules through its AI algorithm. The main aim of this solution is to carry out an automated and interconnected medical diagnostic process to minimize the error in diagnostic results. Also, it can compare analysis between current and previous CT images in order to help the doctor’s workflow.
Product specifications Information source: Vendor
Last updated: March 23, 2022
Product name LuCAS-Plus
Company Monitor Corporation
Subspeciality Chest
Modality CT
Disease targeted Lung cancer
Key-features Nodule detection and localization, nodule characterization, nodule quantification, segmentation and measurement, longitudinal follow-up
Suggested use During: perception aid (prompting all abnormalities/results/heatmaps)
Data characteristics
Population Asymptomatic population, all ages
Input 3D, constrast, non-contrast, CT, low-dose CT
Input format DICOM
Output Nodule coordinate, nodule segmentation, nodule size, nodule type, nodule location, nodule change
Output format DICOM
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Integration via AI marketplace or distribution platform, 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
Certified, Class IIa , MDD
No or not yet
Market presence
On market since 07-2020
Distribution channels
Countries present (clinical, non-research use) 1
Paying clinical customers (institutes) 0
Research/test users (institutes) 2
Pricing model Pay-per-use, Subscription, One-time license fee
Based on Number of users, Number of installations, Number of analyses
Peer reviewed papers on performance
Non-peer reviewed papers on performance
Other relevant papers