RevealAI-Lung

RevealDx
RevealAI™ is Computer Assisted Diagnostic (CADx) software that reveals patterns within images and across patients. The analytics generate scores for the current patient by comparing abnormalities to other patients that have confirmed diagnostics. RevealAI-Lung provides a Malignancy Similarity Index (mSI) from lung CT scans that aids risk assessment. Graphs are provided that help interpret how lung nodules from the current patient relate to confirmed malignant or benign nodules.
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Information source: Vendor
Last updated: Sept. 27, 2022

General Information

General
Product name RevealAI-Lung
Company RevealDx
Subspeciality Chest
Modality CT
Disease targeted Lung cancer
Key-features Nodule malignancy similarity score
Suggested use During: interactive decision support (shows abnormalities/results only on demand)

Technical Specifications

Data characteristics
Population Incidental nodules and those found during screening
Input CT, 3D, contrast, non-contrast
Input format DICOM
Output Malignancy Similarity Index (mSI)
Output format To be determined
Technology
Integration Integration in standard reading environment (PACS)
Deployment Locally on dedicated hardware, Cloud-based
Trigger for analysis On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time < 3 sec

Regulatory

Certification
CE
Certified, Class IIa , MDD
FDA No or not yet
Intended Use Statements
Intended use (according to CE) Design and manufacture of software intended as a decision aid for analysis of computed tomography (CT) scans of the lung for cancer diagnosis.

Market

Market presence
On market since 05-2021
Distribution channels contextflow, Amplify SDK
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes) 2
Pricing
Pricing model Pay-per-use, Subscription
Based on Number of analyses

Evidence

Evidence
Peer reviewed papers on performance

  • Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT (read)
Non-peer reviewed papers on performance
Other relevant papers