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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 CXR2 logo
For 3 Major Radiologic Findings
  • Lung nodule
  • Consolidation
  • Pneumothorax
Lunit INSIGHT CXR2 capture

Background

Chest radiography is one of the most basic and fundamental diagnostic test used in medicine, accounting for 25% of the annual total numbers of diagnostic imaging procedures.1 It has been shown that radiologic information changed clinical practice in more than 60% of those who received chest radiography.2,3 Unfortunately, miss rates for proper interpretation of chest radiographs go as high as 30% even for experts,4,5 leading to increased mortality from treatable diseases.6 Moreover, interpretive performance of chest radiographs differ significantly between specialists and non-specialists, upto 30%.7-9 Among the various diseases detected or diagnosed through chest radiography, lung cancer (nodule/mass), tuberculosis, pneumonia (consolidation), and pneumothorax are among the most common and major diseases.

Product description

Developed using Lunit’s cutting-edge deep learning technology,10 Lunit INSIGHT CXR-MCA accurately detects lung nodule/mass, consolidation, and pneumothorax 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 difficult cases of major chest abnormalities from being missed upon reading chest radiographs.
  • Help physicians make early diagnosis of major chest abnormalities in chest radiographs.
  • Enable non-specialists to perform at specialist level in detecting major chest abnormalities in chest radiographs.

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 98-99% in ROC AUC.11
  • Clinically validated to significantly improve the interpretive capabilities of clinicians and radiologists upto 20%.
  • Currently in preparation for regulatory approval in various markets worldwide, including FDA, CE, MFDS.

Example cases

example case1example case1

CASE #1. A small nodule, diagnosed as lung cancer, is properly detected in the right middle lung field, with an abnormality score of 94%.

example case2example case2

CASE #2. Subtle focal consolidation, diagnosed as pneumonia, is properly detected in the right lower lung field, with an abnormality score of 81%.

example case3example case3

CASE #3. Subtle pneumothorax is properly detected in the left apex, with an abnormality score of 57%.

example case4example case4

CASE #4. Focal consolidation, diagnosed as tuberculosis, is properly detected in the right apex hidden behind the clavicle, with an abnormality score of 72%.

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 Radiation UNSCotEoA. Sources and effects of ionizing radiation: sources: United Nations Publications; 2000.

2 Geijer M, Ivarsson L, Göthlin JH. A retrospective analysis of the clinical impact of 939 chest radiographs using the medical records. Radiol Res Pract 2012;2012.

3 Speets AM, van der Graaf Y, Hoes AW, et al. Chest radiography in general practice: indications, diagnostic yield and consequences for patient management. Br J Gen Pract 2006;56:574-8.

4 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.

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

6 Kesselman A, Soroosh G, Mollura DJ, et al. 2015 RAD-AID Conference on International Radiology for Developing Countries: the evolving global radiology landscape. J Am Coll Radiol 2016;13:1139-44.

7 Monnier-Cholley L, Carrat F, Cholley BP, Tubiana J-M, Arrivé L. Detection of lung cancer on radiographs: receiver operating characteristic analyses of radiologists’, pulmonologists’, and anesthesiologists’ performance. Radiology 2004;233:799-805.

8 Eng J, Mysko WK, Weller GE, et al. Interpretation of emergency department radiographs: a comparison of emergency medicine physicians with radiologists, residents with faculty, and film with digital display. Am J Roentgenol 2000;175:1233-8.

9 Potchen EJ, Cooper TG, Sierra AE, et al. Measuring performance in chest radiography. Radiology 2000;217:456-9.

10 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.

11 ROC AUC Area Under the Receiver Operating Characteristic Curve

12 Seoul National University Hospital, Observer performance study, 201 7