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Products
ChestView
ChestView
GLEAMER
ChestView detects and localizes lesions on Chest X-Rays. It is designed to assist radiologists and clinicians in triaging cases and increasing diagnostic performances by highlighting regions of interest with a plain box (> 90% confidence) or a dotted box (50-90% confidence) and providing a summary table.
Information source:
Vendor
Last updated:
Dec. 18, 2023
General Information
Technical Specifications
Regulatory
Market
Evidence
General Information
General
Product name
ChestView
Company
GLEAMER
Subspeciality
Chest
Modality
X-ray
Disease targeted
Pneumothorax, pleural effusion, alveolar syndrome, nodule, mediastinal mass
Key-features
Triage, detection and localization of pneumothorax, pleural effusion, alveolar syndrome, nodule, mediastinal mass, worklist prioritization
Suggested use
Before: adapting worklist order, flagging acute findings,
During: perception aid (prompting all abnormalities/results/heatmaps)
After: diagnosis verification
Technical Specifications
Data characteristics
Population
All Chest X-rays
Input
Chest X-rays AP, PA, lateral, bed side
Input format
DICOM
Output
Summary table, bounding boxes showing regions of interest
Output format
DICOM SC
Technology
Integration
Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform
Deployment
Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
Trigger for analysis
Automatically, right after the image acquisition
Processing time
10 - 60 seconds
Regulatory
Certification
CE
Certified, Class IIa
, MDR
FDA
No or not yet
Intended Use Statements
Intended use (according to CE)
Software intended to provide preliminary data for helping physicians’ diagnosis of body X-rays
Market
Market presence
On market since
05-2021
Distribution channels
Blackford, Carpl.ai, deepcOS, Incepto, Inframed, Osimis, Papapostolou, Philips AI Manager, RMS Medical Devices, Sectra Amplifier Store, Softway, Eureka Clinical AI, Aidoc aiOS
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 installations, Number of analyses
Evidence
Evidence
Peer reviewed papers on performance
Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs
(read)
Non-peer reviewed papers on performance
Abstract ECR 2023: Evaluation of radiologists’ performance compared to a deep learning algorithm for the detection of thoracic abnormalities on chest X-ray
(read)
Other relevant papers