Chest X-Ray Classifier

QUIBIM

Despite the technological evolution of imaging modalities like CT, US and MRI, conventional X-ray remains the most performed examination in radiology departments and remains a fundamental tool for anatomical analysis in the detection and diagnosis of respiratory diseases and bone tissue alterations. However, radiology departments have limitations in reporting the X-Rays due to the limited resources available. QUIBIM has developed a Chest X-Ray Classification Tool that offers a solution to this problem which can help radiology departments become even more efficient.
Product specifications Information source: Vendor
Last updated: March 10, 2020
General
Product name Chest X-Ray Classifier
Company QUIBIM
Subspeciality Chest
Modality X-ray
Disease targeted atelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia, pneumothorax, consolidation, edema, emphysema, fibrosis, pleural thickening, hernia
Key-features Abonormal probability %, work list triage
Suggested use Before: adapting worklist order, flagging acute findings
During: report suggestion
Without interference of a radiologist: AI-only diagnosis
Data characteristics
Population All chest x-rays
Input Chest X-ray
Input format
Output One-page structured report: abnormality heatmap, abnormality probability, most probable findings
Output format DICOM Secondary Capture / PDF
Technology
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based, Hybrid solution
Trigger for analysis Automatically, right after the image acquisition
Processing time 10 - 60 seconds
Certification
CE Certified, Class IIa
FDA No or not yet
Market presence
On market since 05-2019
Distribution channels
Countries present (clinical, non-research use) 4
Paying clinical customers (institutes) 5+
Research/test users (institutes) <5
Pricing
Pricing model License
Based on Number of installations, Number of analyses
Evidence
Peer reviewed papers on performance Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice. (read)
Non-peer reviewed papers on performance
Other relevant papers