JLD-01K is based on the Convolutional Neural Network (CNN), a type of deep learning model, that detects nodules in pulmonary CT images. Measure the diameter and volume of the nodules found, proceed quantitatively, categorize the LungRADs category, and specify the total number of nodules per patient. In addition, 2D views of Axial, Coronal, Sagittal, etc. are provided through the web-based UI, and lungs and nodules are visualized and displayed through 3D views.
Product specifications Information source: Vendor
Last updated: July 1, 2020
Product name JLD-01K
Company JLK Inc.
Subspeciality Chest
Modality CT
Disease targeted lung nodule
Key-features lung nodules detection and quantification, LungRADs categorization, Vancouver Risk calculator, report generation
Suggested use During: perception aid (prompting all abnormalities/results/heatmaps), report suggestion
Data characteristics
Population all chest CT
Input Lung CT
Input format DICOM, JPG
Output Location annontation, nodule diameter and volume, LungRADs category, Vancouver Risk Calculator
Output format UI, Report
Integration Stand-alone third party application
Deployment Locally on dedicated hardware, Cloud-based, Hybrid solution
Trigger for analysis On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 3 - 10 seconds
CE Certified, Class I
FDA No or not yet
Market presence
On market since 09-2019
Distribution channels
Countries present (clinical, non-research use) 3
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing model Pay-per-use, Subscription
Based on Number of users, Number of installations
Peer reviewed papers on performance
Non-peer reviewed papers on performance
Other relevant papers