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Source: AI And COVID-19  May 24, 2020  3 years, 4 months, 1 week, 1 day, 8 hours, 18 minutes ago

AI And COVID-19: New AI Diagnostic Tool Provides Physicians New Lung Imaging Insights To Treat COVID-19

AI And COVID-19: New AI Diagnostic Tool Provides Physicians New Lung Imaging Insights To Treat COVID-19
Source: AI And COVID-19  May 24, 2020  3 years, 4 months, 1 week, 1 day, 8 hours, 18 minutes ago
AI And COVID-19: Researchers from Princeton University have developed an Artificial Intelligence (AI)  based diagnostic tool to analyze chest X-rays for patterns in diseased lungs. The AI Based platform could give physicians valuable data about an individual's condition, quickly and cheaply, at the point of care.

Professor Jason Fleischer from the faculty  of electrical engineering and the project's main researcher, said he was inspired to create the platform after discovering about COVID-19's devastating range of attacks.
As healthcare facilities have been overrun with patients, physicians have observed two basic types of lung damage, one more immediately life-threatening than the other. Treatment can differ between the types, so distinguishing the two could improve care and better allocate scarce resources.
Current conventional differentiation methods involve expensive and time-consuming procedures, such as computed tomography (CT) scans whereas Professor Fleischer's machine learning model looks at a simple X-ray image and finds patterns that are too subtle even for the expert human eye.
This AI based platform  would give physicians a simple and new measure for determining the type and severity of COVID-19 pneumonia.
Professor Fleischer said, "Importantly, there is no change in practice. The technician doesn't have to do anything differently. Hospitals don't have to do any new procedure. With the X-rays they already have and routinely take we can give them this extra information."
Professor Fleischer and graduate student Mohammad Tariqul Islam have published a research paper that is yet to be peer-reviewed.
Dr Kimani Toussaint, a bioimaging expert and engineering professor at Brown University, who was not involved in the study commented, "Single X-ray scans don't have the type of resolution tomographic X-ray scanning does. However Professor Fleischer's group had identified an important problem with their research, trying to address in a very practical way how to use more readily available X-rays to quickly screen COVID-19 patients, and basically triage them or sort them into the types of treatment they should be getting. I thought it was very nicely done.”
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Professor Fleischer warns  that his tool is not a panacea. His goal is to assist doctors not to replace decision making but to aid it. In this way, machine learning of X-ray images could have a major impact in key areas of the pandemic, and in respiratory di seases beyond COVID-19, such as asthma.
The research is based on a medical article by Dr Luciano Gattinoni, who described the two conditions. Many COVID-19 cases show a familiar form of pneumonia, where the tiny sacs lining a patient's lungs are stiff and heavy with fluid. The stiffness restricts breathing and prevents oxygen transfer to the bloodstream. Treatment for this form involves intubation with a mechanical ventilator, where a computerized machine controls the patient's breathing.
However more than half of the patients look more like an altitude-sick mountaineer: blood-oxygen levels are dangerously low, but the lungs work fairly well and breathing is nearly normal. Perversely, in these cases, mechanical ventilation can damage the lungs, exacerbating the disease. This second category demands a less invasive treatment under Dr Gattinoni's system, such as low-pressure oxygen, repositioning of the body, and the use of a sleep apnea device.
Yet another research, published in late April, Dr Gattinoni and his colleagues wrote: "The wide variation in mortality rates across different intensive care units raises the possibility that the approach to ventilatory management could be contributing to outcome." In short, doctors should determine the correct category of symptoms before putting patients on mechanical ventilators.
Professor Fleischer believes that his technology is useful either way. Machine learning is key to the future of individualized medicine, and Professor Fleischer's X-ray analysis tool is one step along that path. Whether the conditions cited by Gattinoni are two distinct categories or two poles at each end of a smooth spectrum, doctors agree more information would be helpful in deciding whether to place a patient on a ventilator.
Professor Fleischer added, "If you can differentiate who's a favorable responder and who isn't, whether you say it's binary or continuous is almost beside the point. Even if it's continuous, there's benefit."
Dr Gattinoni has said that CT scans are currently the best way to reveal the lung patterns of the disease. But CT scans, which combine many X-ray images from multiple angles into a single picture, are time-consuming and very expensive. Even in well-heeled hospitals, the scanning procedure takes time to schedule and perform. For viral patients, transport to a tomography facility is hazardous both to them and to staff. When human resources are strained, as they have been in hospitals from Queens to Jakarta, these procedures are taxing. In many rural or developing areas, CT is simply not an option.
AI can help doctors make sense of data that is otherwise hard to interpret.
Professor Fleischer further added, "I've been working on machine learning primarily for physics. Imaging through clouds, finding which way fluid will flow in turbulence, etc."
In previous research sponsored by DARPA and the Air Force, he developed AI to analyze noisy images, using algorithms to discover the underlying dynamical equations and predict future motion. Over the past decade he has used this expertise to develop advances in biomedical imaging, including ultrasound technology for ovarian cancer and foot sensors to spot the onset of diabetes.
The new COVID-19 AI tool is designed to process complex information and make it easier to interpret for clinicians in the field, who necessarily have to make decisions with imperfect data, sometimes under extreme duress.
Professor Fleischer hopes the new AI based diagnostic platform using X-rays can give physicians a higher level of confidence when choosing a patient's course of treatment.
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