Product:

RADIFY

Version 3.4.1

Company HQ: 

Johannesburg, South Africa

Demo
Download Product Profile

Last Updated:

26 July 2021

RADIFY detects tuberculosis (TB) on chest X-ray images according to radiology diagnostic standards. It highlights the pathologies related to TB with bounding boxes and heat maps, and provides a summary of the findings and categorizes images into four categories: 1) high probability, 2) intermediate probability, 3) low probability, and 4) no TB found. The images are triaged according to the highest probability of TB.

RADIFY is able to detect other pathologies on the chest X-rays that are unrelated to TB and will also highlight these pathologies with bounding boxes.

RADIFY for teleradiology is a full radiology reporting service offered as an additional service to practices with medical resource challenges.

RADIFY artificial intelligence (AI) models are capable of continual learning. When the Envisionit Deep AI shared cloud environment is used for deployment of RADIFY, continual learning is provided by default. For either offline or hybrid deployments, or installed on a dedicated cloud environment, the continual learning feature may or may not be enabled.

Demo available upon request.

Certification

FDA (pending), CE (pending), SAHPRA certified (class A)

Development Stage

On the market

Deployment

Online, offline, hybrid (offline analysis, on demand synchronization with legacy systems/cloud storage)

Intended Age Group

2+ years

Target Setting

Primary health centres, general hospital (above primary level), teleradiology companies, government/public sector, e.g. national TB programme, private sector, original equipment manufacturers

Current Market

Planned market: Africa, UK, USA

Input

»» Chest X-ray image format: JPEG, PNG, DICOM

»» Chest X-ray type: posterior-anterior chest X-ray, anterior-posterior chest X-ray

Output

»» Heat map

»» Categorized output into 4 categories: 1) high probability, 2) intermediate probability, 3) low probability and 4) TB not found.

»» Location of each abnormality

»» Summarized AI confidence score (AIC) % on each pathology detected


»» Lung findings identified by AI: abscess, atelectasis, blunted costophrenic angle, bronchiectasis, calcification, cavity, consolidation, fibrosis, honeycombing, hyperinflation, lymphadenopathy, mass, nodule, opacity, pleural effusion, pneumothorax, cardiomegaly including cardiothoracic ratio, infiltrates, reticular infiltrates, pneumomediastinum, cysts, bone lesion, micronodules, emphysema, peribronchial thickening


»» Lung findings included in TB score: abscess, calcification, cavity, consolidation, fibrosis, lymphadenopathy, nodule, pleural effusion, prominence in hilar region, infiltrates


»» Disease scores identified: COVID-19, pneumonia, cancer, pneumonia, cardiac disease (cardiothoracic ratio)


»» Report and final conclusion of high, intermediate and low probability of TB


»» Format: JPEG, PNG, DICOM

press to zoom

press to zoom
1/1
ai4hlthlogoNEWtoUse.png

Hardware

Offline deployment: 16 GB RAM, recommended 32 GB for picture archiving and communication system. Dual- or quad-core 64-bit (x86) CPU.
Hybrid deployment: 240 GB SSD main storage, SSD or magnetic storage for image archive. 2TB SSD main storage if continuous training is used. Nvidia Tesla-based GPU for accelerated inference.

Validation

Not needed. The RADIFY platform can be used to read images from any kind of imaging machine (vendor neutral).

Hardware

Offline deployment: 16 GB RAM, recommended 32 GB for picture archiving and communication system. Dual- or quad-core 64-bit (x86) CPU.
Hybrid deployment: 240 GB SSD main storage, SSD or magnetic storage for image archive. 2TB SSD main storage if continuous training is used. Nvidia Tesla-based GPU for accelerated inference.

Server

Online cloud service using Amazon Web Services or Google Cloud Platform. Can be deployed at local server(s) for an offline or hybrid deployment. Please note, servers for offline or hybrid deployments are provided by Envisionit Deep AI at an additional cost.

Integration

It is possible to integrate the product with the client’s legacy picture archiving and communication system. Fee is included in the implementation fee.

Software

Please contact info@envisionit.africa

Processing Time

30 milliseconds

Data Sharing & Privacy

»» Typically data are not shared with the manufacturer. However, if Envisionit Deep AI also provides teleradiology services as part of the engagement, data will be shared within the bounds of the agreement.

»» For both online and offline setups, image de-identification is performed to remove patient and/or hospital identifiers before being processed by the AI.

»» All received DICOM files are anonymized.

»» All identifiers are stored in a secure database on encrypted storage devices, regardless of whether the solution is deployed in the cloud or offline.

»» In case of cloud deployments, all communication to and from the cloud servers is encrypted using latest transport layer security (TLS) encryption with the use of certificates issued by a recognized Certificate Authority.

Price

Please contact info@envisionit.africa

Software Updates

»» Every 2 months. RADIFY AI models are capable of continual learning, and, if enabled, will be trained on more frequently either in the offline/hybrid deployments or dedicated cloud environments. Envisionit Deep AI shared cloud environment provides continual learning by default.

»» Integration with picture archiving and communication system included in the implementation fee.

»» Updates are included in the license fee.

Product Development Method

Supervised deep learning (CNN, RNN)

Training

500 000 CXRs from Southern Africa, India, China, Europe, USA

Reference Standard

Human reader (radiologists specializing in TB), some are confirmed with GeneXpert

Publications

Peer-reviewed publications are not yet available.

Does this page require updates? Send us a message:

This website works best with browsers other than Internet Explorer.