Chest X-Ray Classifier


Quibim’s Chest X-ray Classifier aims to help radiologists deal with the large volumes of chest radiographs by prioritizing potentially pathological cases. This AI-fueled app is able to automatically identify PA/AP acquisitions and estimate the presence probability of 15 different findings in chest radiographs. Once these 15 probabilities are calculated, the classifier combines them to quantify the final abnormal probability of an image. The suite provides heatmaps that are displayed over the analyzed radiographs and that show the level of influence of each region of an image in the final abnormality score. By segmenting both lungs, the tool extracts textural features to foster posterior radiomic studies.
Product specifications Information source: Vendor
Last updated: May 17, 2020
Product name Chest X-Ray Classifier
Company Quibim
Subspeciality Chest
Modality X-ray
Disease targeted Atelectasis, Cardiomegaly, Consolidation, Edema, Emphysema, Enlarged cardiomediastinum, Fibrosis, Fracture, Hernia, Lung lesion, Lung opacity, Pleural effusion, Pleural thickening, Pneumothorax
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
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
Certified, Class IIa , MDD
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 model License
Based on Number of installations, Number of analyses
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