Chest X-ray image,
more effectively
can you read it?
Chest X-ray examination is known as the basic examination included in the general health examination provided by the government and the most commonly performed radiographic examination. It is a very important basic test that can diagnose many diseases among medical images.
Because radiologists manually read X-ray films, such as detecting abnormalities with the naked eye and marking their locations, time is delayed and the amount of reading tasks is accumulated excessively. Is there any way to reduce the burden on doctors and improve work efficiency from this simple and repetitive work?
Here, ‘DEEP:CHEST’ can help you diagnose faster and more accurately.
It advances the performance of chest X-ray examination.
DEEP:CHEST uses artificial intelligence (AI) technology to detect abnormal areas (pulmonary sclerosis, pneumothorax, etc.) If you input a chest X-ray image in DICOM file format, it displays the outline and probability value of the area suspected of lung disease (pulmonary sclerosis, pneumothorax).
It can reduce the burden of reading time.
If you prioritize image reading based on the analysis result data of DEEP:CHEST, and use DEEP:CHEST, you can expect to improve the efficiency of chest X-ray image reading. Enables rapid response to patients who may have high disease severity.
You can focus on the more serious patients first.
By providing anomaly probability information through AI analysis, doctors can quickly filter out normal images and focus on abnormal patients. Especially for radiology residents in emergency rooms, it can simplify diagnosis and shorten read times for emergency patients. This can ultimately speed up the decision-making process and treatment in ED.
DEEP:CHEST-XR-03
Approval from the Ministry of Food and Drug Safety (No. 21-841)
1. AI-related medical images for each disease
It analyzes data and aids in reading by medical institutions and doctors.
2. Quickly locate the disease
Detects and alerts you to anything suspicious.
3. Easy PACS interworking / It is interlocked with the PACS in the hospital through simple DICOM communication.
Display of contours and probability values of suspected areas of cirrhosis
Sensitivity
76.5% More than
Specificity
88% More than
87%
93.6%
Ministry of Food and Drug Safety Clinical Trial Results
Display of contours and probability values of suspected pneumothorax
Sensitivity
75% More than
Specificity
96.3% More than
94.4%
99.63%
Ministry of Food and Drug Safety Clinical Trial Results
Publications
Lee, Sun Yeop, et al. “Localization-Adjusted Diagnostic Performance and Assistance Effect of a Computer-Aided Detection System for Pneumothorax and Consolidation.” Npj Digital Medicine, 2022.
