With the advent and spread of the novel Coronavirus, businesses around the world pitched in to save the World. Amazon and Microsoft created partnerships with governments and nonprofit organizations in launching a relief fund to deal with the pandemic. Facebook made the announcement that it will be supporting the CDC Foundation with matching donations of $10 million as the CDC Foundation prepares to launch a fundraising effort to fight COVID-19 in the United States.
However, it’s worth focusing on Chinese tech leaders Alibaba and Baidu, who paired with scientists from Wuhan to tackle an alternate solution to the problem. They leveraged recent innovations in the AI technology to fight COVID-19, and here are the details.
As the scope of the Coronavirus grew larger and larger, it became common for airports to screen for the virus. Health officials used visual examinations and thermometer guns to inspect people for any signs of COVID-19.
Before long, Alibaba had created a visual inspection system using AI. This system is capable of 96 percent accuracy in using a chest CT scan to detect COVID-19. Using 5,000 COVID-19 cases as the sample group, a deep learning model trained itself and became the decision-making component of this system. In only 20 seconds, this system was not only able to achieve remarkable diagnostic accuracy but was also able to tell the difference between viral pneumonia and COVID-19.
Along a similar timeline, Baidu was able to create an AI system to service sizable public areas. This system can screen as many as 200 people per minute and register an accurate temperature reading. The system uses a suite of cameras with both infrared sensors and computerized vision to scan people. Anyone with a temperature above 37.3 degrees Celsius is flagged by the system.
This system too uses a deep learning algorithm, this one created by a combination of researchers based in the China University of Geosciences, Wuhan University, and Wuhan EndoAngel Medical Technology Company. The algorithm processed over 46,000 CT scans (anonymously submitted) from 106 patients. This group of patients was divided between roughly 51 confirmed Coronavirus pneumonia cases and 55 uninfected control patients, per medRxiv. This model has been able to achieve northward of 95 percent accuracy, which is around the performance benchmark of a highly trained radiologist.
How It Works
These aforementioned cases demonstrate the indisputable value of visual inspection systems based on AI and deep learning algorithms. To reach a stronger high-level understanding of how these systems work, it’s useful to get some background on the algorithm used by deep learning models development for AI visual inspection.
The fundamental concept behind deep learning is teaching machines to learn by example, running large sets of data through artificial neural networks. First, we supply a neural network with specific types of clearly labeled data. Over time, the algorithm identifies common patterns within the data. Then, these commonalities are translated into a mathematical equation or function. Now, future data can be parsed through and classified correctly.
Using deep learning algorithms, computerized visual inspection systems can closely mirror the efforts of humans, identifying, differentiating, and classifying anomalies, characters, and parts.
Data scientists have been able to use these algorithms and the principles behind them to build smart algorithmic processes. These models have been useful to help improve the quality of COVID-19 testing.
Finally, it’s worth noting that visual inspection based on AI technology is far from limited to only the field of healthcare. Even before 2020, there were multiple instances of airline, computer equipment, textile, and automotive industries beginning to use AI in manufacturing for quality inspection and defect detection, to improve workplace safety.
But it has been gratifying to see that, in a time of great crisis and worldwide concern, AI technology has been able to save human life in addition to optimizing workflow.
