ClariPulmo

ClariPi Inc.

ClariPulmo is an AI-powered tri-functional solution for lung CT images. Our deep learning model provides quantitative analysis of low-attenuation and high-attenuation lesions. In combination with ClariCTAI it reduces noise induced bias with 3D reporting.
Product specifications Information source: Vendor
Last updated: Oct. 4, 2023
General
Product name ClariPulmo
Company ClariPi Inc.
Subspeciality Chest
Modality CT
Disease targeted COPD, emphysema, pneumonia, interstitial lung disease, COVID-19.
Key-features Automatic segmentation and visualization of lungs and airways, LAA/HAA quantification, emphysema reporting
Suggested use During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion
Data characteristics
Population No restrictions
Input CT, contrast or non-contrast
Input format DICOM
Output Volume rendering, automated report (two LAA and two HAA reports), heatmap overlay
Output format DICOM SR
Technology
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
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 10 - 60 seconds
Certification
CE
Certified, Class IIa , MDD
FDA
 510(k) cleared, Class II
Market presence
On market since 03-2022
Distribution channels Blackford, Eureka Clinical AI, deepcOS
Countries present (clinical, non-research use) 3
Paying clinical customers (institutes) 5
Research/test users (institutes) 3
Pricing
Pricing model Pay-per-use, Subscription, One-time license fee
Based on Number of installations, Number of analyses
Evidence
Peer reviewed papers on performance

  • Emphysema quantification using low-dose computed tomography with deep learning–based kernel conversion comparison (read)
Non-peer reviewed papers on performance
Other relevant papers