AZChest

AZmed
AZchest is intended for use in the analysis of chest X-rays. It is designed to assist healthcare professionals by automatically detecting, categorizing, and reporting on cardiac and pulmonary abnormalities.
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Information source: Vendor
Last updated: April 7, 2025

General Information

General
Product name AZChest
Company AZmed
Subspeciality Chest
Modality X-ray
Disease targeted Consolidation, Pulmonary edema, Pleural effusion, Pneumothorax, Pulmonary nodule, Rib fracture, Cardiomegaly
Key-features Computer-aided diagnosis tool, intended to help radiologists and emergency physicians to detect and localize abnormalities on standard X-rays
Suggested use Before: stratifying reading process (non, single, double read),
Before: adapting worklist order,
Before: flagging acute findings,
During: perception aid (prompting all abnormalities/results/heatmaps), During: interactive decision support (shows abnormalities/results only on demand),
During: report suggestion,
After: diagnosis verification

Technical Specifications

Data characteristics
Population All patients
Input X-ray
Input format DICOM
Output Images with the regions of interest for the pathology, coordinates of the regions of interest for the pathology, risk score
Output format DICOM
Technology
Integration Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone third party application
Deployment Locally on dedicated hardware, Cloud-based
Trigger for analysis Automatically, right after the image acquisition
Processing time < 3 sec

Regulatory

Certification
CE
Certified, Class IIa , MDR
FDA No or not yet , unknown
Intended Use Statements
Intended use (according to CE)

Market

Market presence
On market since
Distribution channels
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model
Based on

Evidence

Evidence
Peer reviewed papers on performance

  • Evaluation of the performance of an artificial intelligence (AI) algorithm in detecting thoracic pathologies on chest radiographs (read)
Non-peer reviewed papers on performance
Other relevant papers