At Lunit, we develop AI software that can assist radiologists in the interpretation of chest x-rays. The software, Lunit INSIGHT CXR, can discover multiple radiologic findings including lung consolidation, which indicates possible coronavirus (COVID-19) infected pneumonia.
In light of the current outbreak, we have released a special version of Lunit INSIGHT CXR that can support healthcare providers better manage coronavirus. The solution is currently being used to manage coronavirus at hospitals in South Korea and Brazil, providing assistance in patient triage and monitoring.
A medical worker at coronavirus care center located near Daegu, South Korea, is examining a patient’s chest x-ray image with Lunit INSIGHT CXR.
The current coronavirus pandemic is burdening health care providers around the world with massive demands for tests, diagnosis, and treatment. While PCR is the test for definitive diagnosis of coronavirus infection, it can take upto 2 days, if not more, delaying key decision-making for patients. This can be a big problem as delayed diagnosis may lead to further spread of disease.
Chest x-ray can be a fast, effective, and affordable test to evaluate coronavirus-related pneumonia. We suggest the use of AI as a supportive tool for the interpretation of chest x-ray exams, as below.
While ACR and CDC currently do not recommend the use of CT or chest x-ray taken in an enclosed space for the fear of infection via air and surface, ACR states that when medically necessary, facilities may consider deploying portable radiography units since it relatively has lower risk of infection.
In South Korea, Lunit INSIGHT CXR has been deployed to assist the diagnosis of patients in Daegu and nearby region, the hardest-hit area where approximately 8,000 patients—about 85% of the entire domestic cases—has been diagnosed with coronavirus. (as of 30th March, 2020)
One of the major university hospitals in Seoul has built a healthcare center in Daegu, where physicians remotely examine patients and provide consultations via video conference call. The patients’ chest x-ray images are sent directly to radiologists in Seoul, where Lunit INSIGHT CXR is used to support radiologists instantly detect and diagnose coronavirus infection.
The system enables prompt and early discovery of patients, sending them directly to larger hospitals for proper treatment if required.
A healthcare professional in Seoul is examining a patients’ chest x-ray using Lunit INSIGHT CXR.
In Brazil, Lunit INSIGHT CXR has been installed at PreventSenior, one of the largest hospital networks in Brazil with 8 locations throughout the metropolitan region of Sao Paulo. This institution is one of the COVID-19 detection centers that use chest x-ray screening for patients with mild symptoms.
As of late March, the institution has deployed Lunit INSIGHT for the analysis of more than 3,000 chest x-ray images suspected of coronavirus infection. Medical workers here are telling us that Lunit INSIGHT CXR is providing great help “especially for patient triage,” as the hospital is “overflowing with patients while the number of radiologists remains low.”
A medical worker at one of the COVID-19 centers in Brazil is using Lunit INSIGHT CXR to examine a chest x-ray image.
To support healthcare professionals in this pandemic, we are providing free online and instant AI analysis for the detection of radiologic findings suggestive of coronavirus-related pneumonia on chest x-rays. We hope this free access can work as a supportive tool for healthcare providers in the diagnosis and treatment of coronavirus.
The case upload is limited to 20 cases per day per user, but if you need to analyze more cases to accommodate a large volume of coronavirus-suspected patients, please contact us via email at firstname.lastname@example.org and we will provide you with an assistance.
Our preliminary study results show that Lunit INSIGHT CXR is capable of detecting consolidation, which is a major finding that indicates pneumonia, which, in this study, uses data from coronavirus patients. The AI showed performance at a level comparable to that of radiologists.
In our study, we collected six AP (anteroposterior, taking the image from the front side of the chest) x-ray images of six patients with confirmed diagnosis of coronavirus at the time of admission. All patients had pneumonia on chest CT. On x-ray, three patients had clearly visible lesions, subtle in one patient, and not visible in the remaining two patients, when interpreted by a radiologist.
In the retrospective analysis of these images, our goal was to compare the detection performance of human radiologist and AI, to see if our AI would be capable of detecting at human expert-level or more, correctly finding lesions from a chest x-ray of a coronavirus infected patient. The result showed that AI was able to detect what radiologists detected, demonstrating same-level performance.
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