Annalise CXR is an AI clinical decision-support solution for chest X-ray, assisting clinicians to interpret CXR studies by detecting 124 findings. It acts as a second pair of eyes providing notification of suspected findings in under 10 seconds.

Features include:
• analysing up to three images per study including frontal and lateral images
• a confidence bar displaying the likelihood of the finding and uncertainty of the AI model
• customisable user interface that integrates seamlessly into PACS and RIS.
• worklist triage

Available for clinical use in UK, Europe, ANZ and Malaysia
Product specifications Information source: Vendor
Last updated: Jan. 21, 2022
Product name Annalise CXR
Subspeciality Chest
Modality X-ray
Disease targeted 124 findings present in the emergent, urgent, and non-urgent care settings including: air space opacity, interstitial thickening, volume loss, effusions and lung masses, pneumothorax, malpositioned lines and tubes, pneumoperitoneum, acute bony trauma
Key-features Detection of 124 chest findings, worklist triage
Suggested use Before: adapting worklist order, flagging acute findings
During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand)
Data characteristics
Population All chest x-rays for patients over 16 years of age
Input Frontal (PA or AP), plus optional lateral chest X-ray images. Can process up to 3 images in a single study.
Input format DICOM
Output Indication of presence of finding, segmentation overlay, confidence and threshold score/bar
Output format AI Viewer. Worklist – HL7 or API based output for worklist triage (prioritisation)
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application
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 3 - 10 seconds
Certified, Class I , MDD
510(k) cleared, Class II
Market presence
On market since 10-2020
Distribution channels Wellbeing Software
Countries present (clinical, non-research use) 3
Paying clinical customers (institutes) 300+
Research/test users (institutes) 2
Pricing model Subscription
Based on Number of analyses
Peer reviewed papers on performance

  • Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study (read)

  • Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study (read)

Non-peer reviewed papers on performance
Other relevant papers

  • Abstract: Designing Effective Artificial Intelligence Software (read)