Annalise Enterprise CXR

annalise.ai

Key Features

Confidence bar
Detection of up to 124 chest findings + detection of suspected tuberculosis
Normal/abnormal differentiation
Notification
Worklist triage

General Information

Product name

Annalise Enterprise CXR

Company

annalise.ai

Subspeciality

Chest

Modality

X-ray

Disease targeted

124 (+TB) 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; also supporting the detection of tuberculosis, and chronic conditions, such as osteoporosis, chronic heart failure and COPD

Main task

Not specified

Technical Specifications

Population

All chest x-rays for patients over 16 years of age

Patient population age

Not specified

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

Customizable AI Viewer. DICOM SC. 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

Cloud-based, Hybrid solution, Locally on dedicated hardware, Locally virtualized (virtual machine, Docker)

Trigger for analysis

Automatically, Right after the image acquisition

Processing time

10-30 seconds

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIb

Verified by Health AI Register

FDA Certification

Pathway:

510(k) cleared

Class:

Class II

Verified by Health AI Register

Other certifications

Not specified

Market Presence

On market since

10-2020

AI Platforms

Blackford Analysis, Newton's Tree, Nuance, Sectra

Resellers

Not specified

Countries present

40+

Paying clinical customers

300+

Research/test users

10+

Pricing Information

Pricing model

Subscription

Based on

Number of analyses

Evidence & Research

Peer-Reviewed Papers

Peer-Reviewed

View

The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs

Peer-Reviewed

View

Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting

Peer-Reviewed

View

Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction

Peer-Reviewed

View

Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion

Peer-Reviewed

View

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

Peer-Reviewed

View

Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma

Peer-Reviewed

View

Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs

Technical Papers

Technical

View

Poster BIR Annual Congress: Radiologist reporting productivity benefits from AI-assisted triage of CXR studies in clinical practice

Technical

View

Poster RSNA: How normal is a normal chest X-ray: Does a comprehensive artificial intelligence model identify significant findings in chest radiographs interpreted as normal in clinical practice?

Technical

View

Poster UKIO: Insights from implementation of an artificial intelligence assist device across a national radiology network

Technical

View

Poster ECR: Radiologist’s feedback post implementation of a comprehensive AI assist device for CXR across a large radiology network

Technical

View

Poster ECR: Remarkable vs Unremarkable Triage of Chest x-rays based on a comprehensive AI model – validation on a ground truthed, real world dataset.