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Key Features

  • Identifying missed clinical significant findings
  • Quality assurance

General Information

Product name

ChestEye Quality

Company

Subspeciality

Chest

Modality

X-ray

Disease targeted

Algorithms supports 75 different pathologies

Main task

Not specified

Technical Specifications

Population

Patients over 18 years old

Patient population age

Not specified

Input

PA or PA + LAT Digital Chest X Ray

Input format

DICOM

Output

List of possible False Negatives made by radiologists

Output format

DICOM SR or HL7 message

Integration

Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform

Deployment

Cloud-based, Locally on dedicated hardware, Locally virtualized (virtual machine, Docker)

Trigger for analysis

Automatically, Etc., Image upload, On demand, Right after the image acquisition, Triggered by a user through e.g. a button click

Processing time

3 - 10 seconds

Regulatory Information

CE Certification

Pathway:

MDD

Class:

Class IIa

Verified by Health AI Register

Other certifications

Not specified

Market Presence

On market since

09-2021

AI Platforms

Alma Health Platform, Blackford Analysis, CARPL.AI, Deepc, Sectra

Resellers

Not specified

Countries present

9

Paying clinical customers

Not specified

Research/test users

Not specified

Pricing Information

Pricing model

Pay-per-use, Subscription

Based on

Number of analyses, Number of installations

Evidence & Research

Peer-Reviewed Papers

Peer-Reviewed

View

Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software

Peer-Reviewed

View

Real-world testing of an artificial intelligence algorithm for the analysis of chest X-rays in primary care settings

Peer-Reviewed

View

Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation

Peer-Reviewed

View

Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction

Source: vendor | First published: Sep 4, 2024 | Last updated: Jul 9, 2025