Last Updated:
15th May 2023
ChestEye and ChestLink are diagnostic aid tools and triaging solutions. The goal of these products is to save time for radiologists by identifying chest X-ray images that have no abnormalities. ChestEye and ChestLink are based on the same artificial intelligence (AI) algorithm but use different threshold scores. ChestLink is used for normal reporting automation, while ChestEye is used for prioritization.
ChestEye can prioritize cases with abnormal images, including those with a high likelihood of having tuberculosis (TB) and also provide preliminary reports for them.
ChestLink can also provide diagnostic aid by automatically issuing up to 40% of reports for cases without abnormalities on the chest X-ray. As a result, radiologists would not need to spend any time on these cases.
Certification
ChestEye: CE Class IIB
ChestLink: CE Class IIB
Development Stage
On the Market
Deployment
Online & Offline
Intended Age Group
18+ years
Target Setting
Primary health centres, general hospital (above primary level), teleradiology companies, government/public sector, e.g. national TB programme, private sector
Current Market
Europe, South America, Australia, Asia, Middle East, and Africa
Input
The product can be used to read images from any kind of chest X-ray machine.
Chest X-ray image format: JPEG, PNG, DICOM
Chest X-ray type: ChestEye: posterior-anterior chest X-ray and anterior-posterior chest X-ray, ChestLink: posterior-anterior chest X-ray only
Output
Output includes:
Heat map
Dichotomous output indicating whether each abnormality is present or absent
Dichotomous output only indicating whether TB is likely present or likely absent
Location of each abnormality
The cutoff/threshold score can be adjusted/optimized for each client. Results are provided in a structured report.
Lung abnormalities detected include:
abscess, airfluid level, atelectasis, blunted costophrenic angle, bronchiectasis, calcification, cavity, consolidation, fibrosis, hyperinflation, interstitial markings, loculated pleural effusion, lymphadenopathy, mass, nodule, opacity, pleural effusion, prominence in hilar region, pneumothorax, tracheal shift
Lung abnormalities included in TB score:
calcification, cavity, consolidation, fibrosis, lymphadenopathy, opacity, pleural effusion, prominence in hilar region
Hardware
Hardware requirements:
16 GB of RAM
64bit CPU with 8 logical cores with AVX instruction support
1 TB of disk space
Server
Internet Bandwidth (FTTH): >= 100 Mpbs
Hardware
Hardware requirements:
16 GB of RAM
64bit CPU with 8 logical cores with AVX instruction support
1 TB of disk space
Integration with X-ray Systems
Integration with PACS and Legacy Systems
Please contact info@oxipit.ai
Please contact info@oxipit.ai
Software
Linux (preferably Ubuntu 18.04 LTS) with inbound SSH access
Processing Time
Up to 10 seconds
Data Sharing & Privacy
Server location (for online product):
Servers could be located by the need of the client
Whether data is shared with the manufacturer depends on agreement with the client
There is an option to de-identify data. Additional software installation is required.
Price
Please contact info@oxipit.ai for more information on price.
Oxipit software integrates with picture archiving and communication systems (PACS) and/or radiology information system (RIS) via either DICOM or HL7 protocols. Images are automatically routed to the Oxipit server and after analyses, Oxipit provides structural reports with priority back to PACS/RIS.
Software Updates
Frequency: Monthly
Cost: All costs are included in the pricing
Product Development Method
Supervised deep learning (CNN, RNN)
Training
1 million chest X-rays from both non-public and open source datasets from Europe, Asia and South and North America.
Reference Standard
Majority human reader (multi-read), also some examples with computed tomography (CT)
Publications
Peer-reviewed publications are not yet available.
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