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.
gleamer-ai-chestview
Information source: Vendor
Last updated: October 29, 2024

General Information

General
Product name ChestView
Company GLEAMER
Subspeciality Chest
Modality X-ray
Disease targeted Pneumothorax, pleural effusion, consolidation, nodule, mediastinal or hilar mass
Key-features Triage, detection and localization of pneumothorax, pleural effusion, consolidation, nodule, mediastinal or hilar 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 Adults and Children (> 15 years old)
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 AGFA, Aidoc, Blackford, Carpl.ai, deepcOS, Ferrum, Fujifilm, Incepto, Microsoft (Nuance Communications), Sectra Amplifier Store, Siemens Healthineers, Eureka Clinical AI
Countries present (clinical, non-research use) >36
Paying clinical customers (institutes) >350
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)

  • Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation
    (read)
Non-peer reviewed papers on performance

  • Abstract ECR 2023 (RPS 504-2): Evaluation of radiologists’ performance compared to a deep learning algorithm for the detection of thoracic abnormalities on chest X-ray (read)
Other relevant papers