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Lunit INSIGHT CXR

Lunit

AI solution for radiology that detects 10 radiologic findings with high accuracy on chest x-rays

Developed using Lunit’s cutting-edge deep learning technology, Lunit INSIGHT CXR accurately detects 10 of the most common findings in a chest x-ray, which includes atelectasis, calcification, cardiomegaly, consolidation, fibrosis, mediastinal widening, nodule, pleural effusion, pneumoperitoneum, and pneumothorax. The AI (Artificial Intelligence) solution generates (1) location information of detected lesions in the form of heatmaps, (2) abnormality scores reflecting the probability that the detected lesion is abnormal, and (3) an AI “case report” that summarizes the analysis result by each finding. The solution is indicated to be directly involved in the primary interpretation process of radiologists or clinicians.

 

Value Proposition

  • Accurate reading of chest radiographs, with max. 20% increase in reading performance
  • Improved workflow efficiency with triage, reducing reading time by 34%
  • Supportive tool for evaluation of COVID-19 related pneumonia, used for triage or monitoring

 

Training & Validation

  • Trained with a large-scale (>200,000 cases), high-quality (clinically/CT-proven cases) training set
  • Demonstrated to perform at a standalone accuracy of 97-99% in ROC AUC
  • Certified with CE Mark and approved by Korea MFDS
  • Currently in preparation for regulatory approval in various markets worldwide, including FDA

 

Medical Publications

  • Test-Retest Reproducibility of a Deep Learning-Based Automatic Detection Algorithm for the Chest Radiograph, Kim HJ et al., European Radiology 2019
  • Deep Learning for Chest Radiograph Diagnosis in the Emergency Department, Hwang EJ, Nam JG, et al., Radiology 2019
  • Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs, Hwang EJ, Park SG, et al., JAMA Network Open 2019
  • Development and Validation of a Deep Learning-based Automated Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs, Hwang EJ, Park SG, et al., Clinical Infectious Diseases 2018
  • Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs, Nam JG, Park SG, et al., Radiology 2018

 

**THIS PRODUCT IS NOT FDA 510(K) CLEARED, NOT FOR SALE IN THE U.S.**


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