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

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