SenseCare-Lung Pro

SenseTime
SenseCare Lung CT automatically detects pulmonary nodules and pneumonia (including COVID-19) lesions and provides analysis such as lesion classification, risk evaluation, quantification, and structured reports for radiologists. Based on 3D rendering technology, it can also provide a 3D reconstruction.
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Information source: Vendor
Last updated: August 26, 2021

General Information

General
Product name SenseCare-Lung Pro
Company SenseTime
Subspeciality Chest
Modality CT
Disease targeted Lung cancer, pneumonia, COVID-19
Key-features Lung nodule detection, nodule type classification (solid, GGO etc.), pneumonia detection, key parameter quantification, nodule tracking over time, automatic report generation
Suggested use During: perception aid (prompting all abnormalities/results/heatmaps), report suggestion

Technical Specifications

Data characteristics
Population All chest CTs, all ages
Input Non enhanced CT, slice thickness compatible with <= 5mm, prefered <= 1.5mm
Input format DICOM
Output Image annotations, key parameter quantification, follow up analysis, report based on AI findings
Output format DICOM
Technology
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical 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 IIb , MDR
FDA No or not yet
Intended Use Statements
Intended use (according to CE)

Market

Market presence
On market since 10-2020
Distribution channels
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Pay-per-use, Subscription
Based on Number of users, Number of installations, Number of analyses

Evidence

Evidence
Peer reviewed papers on performance
Non-peer reviewed papers on performance
Other relevant papers