Date: 2nd March 2020
The clinical manifestations of COVID-19 pneumonia are complicated and can be characterised as fever, cough, myalgia, headache, and the onset of gastrointestinal symptoms. Whilst rapid detection kits for diagnosing COVID-19 are becoming more readily available, computed tomography (CT) scans are still the most efficient way to diagnose and evaluate the severity of the pneumonia.
Now a team from Renmin Hospital of Wuhan University, China, led by Honggang Yu, has constructed and validated a system based on deep learning for the identification of COVID-19 pneumonia on high resolution CT. The model has comparable performance with expert radiologists but is considerably faster.
In the early days of the COVID-19 outbreak the team observed a concerning bottleneck with traditional diagnosis, as thousands of patients queued for CT scans. With many patients requiring in depth, slow scans, radiologists were overloaded leading to massive delays in diagnosis, isolation of patients, and subsequent patient treatment and prognosis. The team – no strangers to AI – had previously used it to detect minor lesions in gastrointestinal endoscopy. They therefore reasoned that, by applying deep learning (DL) to detect tiny features during image analysis, it could provide a highly valuable diagnostic tool for radiologists and doctors, and contribute to the control of disease.
In the pre-print paper published on medRxiv, the team have developed, and validated a deep learning model.
We are currently seeing a massive drive around the world to slow down the spread of COVID-19. By trying to contain the disease, it gives us time to find effective treatments or cures, and to allow the healthcare systems time to prepare and not become overwhelmed by the volume of patients.
We have already seen the power that AI can bring to the epidemiology of COVID-19, from predicting the spread of infections, using data to understand the nature of the virus, to aiding complex decision making. AI-driven vaccines and drug discovery could vastly increase the rate of effective treatment development and implementation. Now, here, the speed of diagnosis, will also hopefully aid the containment, and allow the rapid treatment, and isolation of those infected; a crucial factor in patient prognosis.
Chen, J., L. Wu, J. Zhang, L. Zhang, D. Gong, Y. Zhao, S. Hu, Y. Wang, X. Hu, B. Zheng, K. Zhang, H. Wu, Z. Dong, Y. Xu, Y. Zhu, X. Chen, L. Yu and H. Yu (2020). “Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study.” medRxiv: 2020.2002.2025.20021568.