Coronavirus disease 2019 (COVID-19) is a newly identified virus that has caused a recent outbreak of respiratory illnesses starting from an isolated event to a global pandemic. As of July 2020, there are over 2.8 million confirmed COVID-19 cases in the U.S. and over 11.4 million worldwide. In the United States alone, over 130,000 Americans have died from COVID-19, with no end in sight. A major cause of this rapid and seemingly endless expansion can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. The lack of optimized tools necessary for rapid mass testing produces a ripple effect that includes the health of your loved ones, jobs, education, and on the national level, a country’s Gross Domestic Product (GDP), but artificial intelligence and ultrasound may help.
STATE OF ART IN DIAGNOSIS
Currently, there are two types of tests that are conducted by healthcare professionals–diagnostic tests and antibody tests. The diagnostic test, as the name implies, helps diagnose an active coronavirus infection in a patient. The ideal diagnostic test and the “gold standard” according to the United States Center for Disease Control (CDC) is the Reverse Transcription Polymerase Chain Reaction, or simply, RT-PCR. RT-PCR is a molecular test not only capable of diagnosing an active coronavirus infection, but it can also indicate whether the patient has ever had COVID-19 or were infected with the coronavirus in the past. However, the time required to conduct the test limits its effectiveness when mass deployed.
A much faster but less reliable diagnostic test alternative to RT-PCR is an antigen test. Much like the gold standard, the antigen test is capable of detecting an active coronavirus infection in a much shorter timeframe. Although antigen tests produce rapid results, usually in about an hour, the results are deemed highly unreliable, especially with patients who were tested negative according to the US FDA.
In contrast, the antibody test is designed to search for antibodies produced by the immune system of a patient in response to the virus and is limited by its ability to only detect past infections, which is less than ideal to prevent an ongoing pandemic.
To combat the rapid expansion of an airborne virus such as COVID-19, or future variations of a similar virus, rapid and reliable solutions must be developed that aim at improving the limitations of current methods. Although highly accurate, methods such as RT-PCR do not meet the speed requirements needed for testing on a large scale. Depending on the location, diagnosis of an active coronavirus infection with RT-PCR may take anywhere between several hours and up to a week. When the number of daily human-to-human interactions are considered, the lack of speed in diagnosing an active coronavirus patient could be the difference between a pandemic or an isolated local event.
As an alternative to molecular tests, Computed Tomography (CT) scans of a patient’s chest have shown promising results in detecting an infection. However, in addition to not being recommended by the CDC to diagnose COVID-19 patients, there are many unwanted consequences with the use of CT scans. With CT scans used to diagnose multiple illnesses, some of which relate to serious emergencies such as brain hemorrhaging, they cannot be used as the primary tool for diagnosing COVID-19. This is especially true in rural areas where the healthcare infrastructure is underfunded. Mainly due to the required deep cleaning of the machine and room after each patient, which usually requires 60 to 120 minutes, many institutions are unable to provide CT scans as a viable primary diagnostic tool. Ultimately, given the need for CT scanners for several other health complications combined with limited patient capacity at each hospital, alternative methods must be developed to diagnose an active coronavirus patient.
Recently Point-of-Care (POC) devices have started to be adopted by many healthcare professionals due to its reliability and portability. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale.
Working since mid-March, when early cases of physicians adopting mobile ultrasound technology emerged, the research team at The Ohio State University, Dr. Alper Yilmaz and PhD student Shehan Perera, started developing a solution that can automate an already well-established process. Dr. Yilmaz is the director of the Photogrammetric Computer Vision lab at Ohio State. Dr. Yilmaz’s expertise in machine learning, artificial intelligence, and computer vision combined with the research experience of Shehan Perera laid a strong foundation to tackle the problem at hand. As it stands, the screening of a new patient, with the use of a mobile ultrasound device takes about 13 minutes, with the caveat that it requires a highly trained professional to interpret the results generated by the device. With the combination of deep learning and computer vision, the research team was able to use data generated from the ultrasound device to accurately identify COVID-19 cases. The current network architecture, which is the product of many iterations, is capable of detecting the presence of the virus in a patient with a high level of accuracy.
Many fields have been revolutionized with modern deep learning and computer vision technologies. With the methods developed by the research team, this technology can now allow any untrained worker to use a handheld ultrasound device, and still be able to provide a service that rivals that of a highly trained doctor. In addition to being extremely accurate, the automated detection and diagnosis process takes less than 10 minutes, which includes scanning time, and sanitation is as simple as removing a plastic seal that covers the device. The benefits of this technology can not only be useful for countries such as the United States, with a well-established healthcare system, but, more importantly, can significantly help countries and areas where medical expertise is rare.
The United States healthcare system is among the best in the world, yet we are failing to provide the necessary treatment patients clearly need. The developments made in artificial intelligence, deep learning, and computer vision offer proven benefits, which can not only be leveraged to improve the current state of the global pandemic but can lay the foundation to prevent the next. Alternative testing methods such as mobile ultrasound devices combined with novel artificial intelligence algorithms that allow for mass production, distribution, and testing could be the innovation that could help decelerate the spread of the virus, reducing the strain on the global healthcare infrastructure.
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Interested in learning more about COVID-19 or AI? Check out the following posts from the Scan:
- Do It With Heart: Pre-Intubation Point-of-Care Echocardiography for Hemodynamic Optimization
- My Sonography Experience With COVID-19
- Artificial Intelligence and Point-of-Care Ultrasound