InferRead DR Chest

Infervision

InferRead DR Chest aims to detect diverse pathologies in a single X-Ray image. It is able to detect 14 different pathologies including pneumonia, tuberculosis, fracture, nodules, pleural effusion or pulmonary infection among others of high interest. This solution is aimed at detecting incidental findings and at those cases where rapid and cost-effective diagnose must be made such as the emergency rooms or small healthcare centers where CT scans are not available or where a second reading is required.
Product specifications Information source: Vendor
Last updated: April 1, 2020
General
Product name InferRead DR Chest
Company Infervision
Subspeciality Chest
Modality X-ray
Disease targeted lung cancer, pneumothorax, fracture, tuberculosis, lung infection, aortic calcification, cord imaging, heart shadow enlargement, pleural effusion.
Key-features abnormality detection
Suggested use Before: adapting worklist order
During: interactive decision support (shows abnormalities/results only on demand)
Data characteristics
Population any
Input Chest X-ray
Input format DICOM
Output lesions name, lesion location, degree of abnormality
Output format DICOM overlay, pdf file (draft report), DICOM GSPS, webviewer (description of lesion features)
Technology
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Stand-alone third party application, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker)
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 3 - 10 seconds
Certification
CE Certified, Class IIa
FDA No or not yet
Market presence
On market since 01-2020, 2016 (China)
Distribution channels
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Subscription
Based on Number of installations
Evidence
Peer reviewed papers on performance
Non-peer reviewed papers on performance
Other relevant papers