Lunit INSIGHT CXR
LunitProduct specifications |
Information source:
Vendor
Last updated: Aug. 20, 2023 |
General | |
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Product name | Lunit INSIGHT CXR |
Company | Lunit |
Subspeciality | Chest |
Modality | X-ray |
Disease targeted | Atelectasis, calcification, cardiomegaly, consolidation, fibrosis, mediastinal widening, nodule, pleural effusion, pneumothorax, pneumoperitoneum, tuberculosis |
Key-features | Radiologic finding detection, abnormality score, text interpretation |
Suggested use | Before: adapting worklist order, flagging acute findings During: perception aid (prompting all abnormalities/results/heatmaps), report suggestion |
Data characteristics | |
Population | Patients aged 14 years or older |
Input | chest PA(posterior-anterior view), chest AP(anterior-posterior view) |
Input format | DICOM |
Output | Localization (color map, grayscale map, combined map, single colormap), abnormality score for each lesion/case, binary assessment of abnormality, worklist order, draft radiology report generation |
Output format | DICOM Secondary Capture, DICOM GSPS (Grayscale Softcopy Presentation State), Prepopulated Report (API integration only), HL7 |
Technology | |
Integration | Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased |
Deployment | Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based |
Trigger for analysis | Automatically, right after the image acquisition |
Processing time | 3 - 10 seconds |
Certification | |
CE
|
Certified,
Class IIa
, MDR
|
FDA
|
510(k) cleared, Class II |
Market presence | |
On market since | 11-2019 |
Distribution channels | GE Healthcare (Thoracic Care Suite), Philips Healthcare, INFINITT AI Platform, FujiFilm, Agfa, CARPL.ai, Osimis, Blackford |
Countries present (clinical, non-research use) | |
Paying clinical customers (institutes) | |
Research/test users (institutes) | |
Pricing | |
Pricing model | |
Based on | |
Evidence | |
Peer reviewed papers on performance |
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Non-peer reviewed papers on performance | H Yoo, et al. Deep learning to increase lung cancer detection in chest x-rays: a retrospective cohort analysis of national lung screening trial participants. ECR. 2020 S Na, et al. On the robustness of a deep learning-based algorithm for detecting abnormalities in chest radiographs across different devices and view positions: a retrospective case-control study. ECR. 2020 JH Lee, et al. RSNA. Deep-Learning based Automated Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs: Diagnostic Performance in Systematic Screening of Asymptomatic Individuals. 2019 EJ Hwang, et al. Deep Learning Algorithm for Surveillance of Pneumothorax after Percutaneous Transthoracic Lung Biopsy: Validation in Multi-Center, Consecutive Cohorts. RSNA. 2019 M Kim, et al. Development of a Deep Learning-Based Algorithm for Independent Detection of Chest Abnormalities on Chest Radiographs. RSNA. 2019 S Park, et al. Deep Learning-Based Automatic Detection Algorithm for the Detection of Major Thoracic Abnormalities on Chest Radiographs. RSNA. 2018 EJ Hwang, et al. Performance Validation of a Deep Learning-Based Automatic Detection Algorithm for Major Thoracic Abnormalities on Chest Radiographs. RSNA. 2018 J Aum, et al. Multi-Stage Deep Disassembling Networks for Generating Bone-Only and Tissue-Only Images from Chest Radiographs. RSNA. 2018 EJ Hwang, et al. Development and Validation of a Deep Learning-Based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs. RSNA. 2018 JG Nam, et al. Automatic Detection of Malignant Pulmonary Nodules on Chest Radiographs Using a Deep Convolutional Neural Network: Detection Performance and Comparison with Human Experts. RSNA. 2017 S Park, et al. Deep Learning-based Automatic Detection Algorithm for the Detection of Malignant Pulmonary Nodules on Chest Radiographs. RSNA. 2017 HJ Kim, et al. Performance Assessment of Data-driven Imaging Biomarker for Screening Pulmonary Tuberculosis on Chest Radiographs. RSNA. 2016 CM Park, et al. Deep Convolutional Neural Network Approaches in Making a Diagnosis with Chest Radiographs: Initial Experience. RSNA. 2016 |
Other relevant papers |
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