A research team led by the Head of Department of Statistics and Actuarial Science, the University of Hong Kong, and the Assistant Professor of Centre of Statistical Research, School of Statistics, Southwestern University of Finance and Economics has integrated radiography and computer vision to develop a digital online diagnostic system for COVID-19 based on chest CT scans.
The diagnostic system can help to screen suspected cases of COVID-19 and evaluate the probability of one contracting the disease. It has the following features:
- Fast – the diagnostic result is immediate from chest CT images shown
- Accurate -the solution has an accuracy of 88%, AUC (performance measurement for binary classification model) of 93%, Sensitivity – 86%, Specificity – 90%
- Easy to Use – Online web with a user-friendly interface
- Open Source – All codes and data are freely available
With years of research experience in Biostatistics and Clinical Trials, the team have been actively extending AI technologies to applications in the medical field in recent years. Meanwhile, the use of chest CT scans for screening suspected cases has been common in the research of various diseases.
They decided to perform the diagnosis based on chest CT scans with reference to their many years of research in the field of Computer Vision. There are many issues with the current RT-PCR testing for COVID-19 in terms of false negatives and time lag in diagnosis.
The test, which takes a swab from an individual’s nose or throat for a trace of the virus, sometimes requires several trials to make a final confirmation.
This would put patients at a great disadvantage, as they cannot be diagnosed in a fast way and be provided with the necessary quarantine and treatment at an early stage.
As discovered in radiological research, CT scanning may be effective in testing for COVID-19, particularly amongst those with no symptoms or minimal symptoms.
“This is because the coronavirus will typically first attack the lungs and cause lesions after it enters the body. By integrating AI technologies, we use patients’ chest CT images for early diagnosis. However, since most of the chest CT datasets of COVID-19 patients are not publicly shared, we have to spend much time to search for publicly available samples and tag them,” added the Professor.
Building this digital platform is more proof that Radiography and Computer Vision can be perfectly integrated, actualising the practical use of AI technologies in medical fields.
The major difference between the current batch of CT images and the traditional medical imaging dataset is that each of the CT samples is collected from a research preprint.
In these papers, clinical experts have comprehensively annotated the chest CT images of the COVID-19 patients with detailed lesion descriptions.
Leveraging on these text reports from 760 research papers, the research team further analysed and pinpointed five different lesions in association with COVID-19 and identified each confirmed patient with at least one of the five lesions or more. These five lesions are the distinctive features that differentiate COVID-19 from general pneumonia or other lung diseases.
In this regard, the research team at HKU has designed a lesion-attention deep neural network (LA-DNN) model based on the CT images.
Whilst the proposed data-driven LA-DNN model focuses on the primary task of binary classification for COVID-19 diagnosis, an auxiliary multi-label learning task is implemented simultaneously to draw the model’s attention to the five lesions of COVID-19.
As both tasks are trained synchronously while it shows that the auxiliary task promotes the primary task to focus its attention on the lesion areas and, as a result, the diagnostic accuracy of COVID-19 can be improved drastically.
After launching the online COVID-19 diagnostic system, the research team will continue to collect new samples and improve the training model periodically. Professor Yin and Dr Liu hope that medical staff battling with the disease can make use of the diagnostic system and share patients’ image data, to initiate collaborative research and accommodate the urgent demands for COVID-19 testing.
Currently, most of the research papers do not share the data and the computer codes, and this does not facilitate knowledge exchange and disease prevention around the globe, yet our online system, data and computer codes are all publicly and freely available for everyone in the world.
