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.

Information source: Vendor
Last updated: Aug. 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