Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests have emerged as the standard for testing and confirming COVID-19 infections, with nearly 80 million performed in the US thus far since the start of the pandemic. Additionally, methods such as chest x-rays can complement these tests for a more robust picture of infection. However, according to new research published in PLOS ONE, RT-PCR testing and other mechanisms for confirming infection can be ineffective or even unavailable in certain locations, especially in poorer countries, making accurate and timely reporting of COVID-19 infection difficult.
According to the study published in PLOS ONE, researchers are looking at a way to improve machine learning’s ability to predict COVID-19 infections without a full set of diagnostic data, called the denoising fully connected network (DFCN). The authors of the study concluded that “in the context of the current pandemic and the limitations of the RT-PCR test, this study introduces a model that is accurate and applicable for use in many diverse settings around the world.”
The DFCN is designed to work well in the absence of complete data inputs, which may be beneficial given the inconsistency in which some clinical information is not available or able to be collected about COVID-19. Specifically, researchers note that “the model is trained with randomly masked inputs which, combined with this unique regularization method, instills a robustness to missing input data.”
The study first gathered information about COVID-19 patients, including chest x-rays and other laboratory information to train the DFCN on how to recognize and predict COVID-19 infections. Chest x-rays specifically, were chosen because of the ways COVID-19 can present similarly to pneumonia.
Then, to compare the DFCN's abilities with other types of machine learning used to make predictions in clinical contexts, researchers used data from an ablation study, including imaging scans and laboratory information. Their findings highlighted the superiority of the DFCNs abilities, suggesting their model may be more effective for predicting COVID-19 infections from limited data.
In their analysis of the DFCN compared to other machine learning networks, the researcher team highlighted that denoising autoencoders are a common approach used in these machine learning applications in clinical contexts. The problem, however, is that autencoders in most machine learning tools, such as the fully convolutional network (FCN), create a bottleneck “efficiency” constraint. The DFCN overcomes this limitation.
More research, including more data sets, could help prove and validate the DFCNs capabilities for predicting clinical information in the absence of complete data.