InferRead DR Chest

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.
Information source: Vendor
Last updated: March 24, 2024

General Information

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)

Technical Specifications

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)
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


Certified, Class IIa , MDD
FDA No or not yet
Intended Use Statements
Intended use (according to CE) The design, Development and Manufacture of Computer aided diagnostic software for viewing and analyzing DICOM images to assist physicians with abnormality detection and diagnosis.


Market presence
On market since 01-2020
Distribution channels
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing model Subscription
Based on Number of installations


Peer reviewed papers on performance

  • Doctor’s Orders—Why Radiologists Should Consider Adjusting Commercial Machine Learning Applications in Chest Radiography to Fit Their Specific Needs (read)

  • Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction (read)

Non-peer reviewed papers on performance
Other relevant papers