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AI analysis for Chest x-ray

Insight CXR

AI analysis for Mammography

Insight MMG

AI analysis for Tissue Slides

Insight SCOPE
MFDS Mark
Lunit INSIGHT CXR1 logo
For Nodule Detection
Lunit INSIGHT CXR1 capture

Background

Proper detection of lung nodules, which includes lung cancer, is a challenging task when interpreting chest radiographs, with miss rates reported to be 20-30%. 1, 2 This is especially true for radiologists who need to read high volumes of images at limited amount of time, as well as for non-specialists who lack expertise in reading difficult cases, such as chest radiographs of small or hidden nodules. Missed lung cancer has serious clinical implications, with over 50% in reduction of 5-year survival rate when left undetected for around 1 year.3

Product description

Developed using Lunit’s cutting-edge deep learning technology,4 Lunit INSIGHT CXR-Nodule accurately detects lung nodules in the form of diagnostic support tool. The AI solution generates (1) location information of detected lesions in the form of heatmaps and (2) abnormality scores reflecting the probability that the detected lesion is abnormal. The solution is indicated to be directly involved in the primary interpretation process of radiologists or clinicians.

Primary value proposition

  • Prevent nodules, especially small or hidden nodules, from being missed upon reading chest radiographs.
  • Help physicians make early diagnosis of lung cancer in chest radiographs.
  • Enable non-specialists to perform at specialist level in detecting lung nodules in chest radiographs.

Training & Validation

  • Trained with a large-scale (>70,000 cases), high-quality (clinically/CT-proven cases) training set.
  • Demonstrated to perform at a standalone accuracy of 97% in ROC AUC.5
  • Clinically validated to significantly improve the interpretive capabilities of clinicians and radiologists upto 20%.
  • Initial observer performance study published in Radiology.6
  • MFDS approved for clinical use in Korea (Computer-aided detection software; Approval No.18-574).

Example cases

example case1example case1

CASE #1. A nodule, diagnosed as lung cancer, hidden behind the heart is properly detected, with an abnormality score of 44%. This case was missed by 8 out of 15 radiologists.

example case2example case2

CASE #2. A nodule, diagnosed as lung cancer, in the right upper lung field is properly detected, with an abnormality score of 66%. This case was missed by 9 out of 9 radiologists.

example case3example case3

CASE #3. A nodule, diagnosed as lung cancer, hidden behind the diaphragm is properly detected, with an abnormality score of 96%. This case was missed by 5 out of 9 radiologists.

Journals & Conference
Abstracts

  • Nam JG, Park SG, et al. Development and Validation of Deep Learning-Based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs, Radiology 2018 (in press)
  • Development and Validation of a Deep Learning-Based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs, RSNA 2018
  • Multi-Stage Deep Disassembling Networks for Generating Bone-Only and Tissue-Only Images from Chest Radiographs Performance Validation of a Deep Learning-Based Automatic, RSNA 2018
  • Detection Algorithm for Major Thoracic Abnormalities on Chest Radiographs, RSNA 2018
  • Deep Learning-Based Automatic Detection Algorithm for the Detection of Major Thoracic Abnormalities on Chest Radiographs, RSNA 2018
  • Automatic Detection of Malignant Pulmonary Nodules on Chest Radiographs Using a Deep Convolutional Neural Network: Detection Performance and Comparison with Human Experts, RSNA 2017
  • Deep Learning-based Automatic Detection Algorithm for the Detection of Malignant Pulmonary Nodules on Chest Radiographs, RSNA 2017

References

1 Quekel LG, Kessels AG, Goei R, van Engelshoven JM. Miss rate of lung cancer on the chest radiograph in clinical practice. Chest 1999;115:720-4.

2 Forrest JV, Friedman PJ. Radiologic errors in patients with lung cancer. Wes J Med 1981;134:485.

3 Detterbeck FC, Gibson CJ. Turning gray: the natural history of lung cancer over time. J Thor Oncol 2008;3:781-92.

4 Lunit’s high-end deep learning technology has been demonstrated in various international competitions - won World #1 in MICCAI TUPAC 2016, and CAMELYON 2017; Recognized as one of the world's top 100 AI startups by CB Insights in 2017.

5 ROC AUC Area Under the Receiver Operating Characteristic Curve

6 Seoul National University Hospital, Observer performance study, 2017

7 Nam JG, Park SG, et al. Development and Validation of Deep Learning-Based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs. Radiology 2018; (in press).