We developed and validated a machine learning diagnostic model for novel coronavirus (COVID-19) disease, integrating artificial-intelligence-based computed tomography (CT) imaging and clinical features.
We conducted a retrospective cohort study in 11 Japanese tertiary care facilities that treated COVID-19 patients. Participants were tested using both real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CT between January 1 and May 30, 2020. We chronologically split the dataset in each hospital into training and test sets, containing patients in a 7:3 ratio. Light Gradient Boosting Machine model was used for analysis.
A total of 703 patients were included with two models — the full model and the A-blood model — developed for their diagnosis. The A-blood model included eight variables (the Ali-M3 confidence, along with seven clinical features of blood counts and biochemistry markers). The areas under the receiver-operator curve of both models (0.91, 95% confidence interval (CI), 0.86 to 0.95 for the full model and 0.90, 95% CI, 0.86 to 0.94 for the A-blood model) were better than that of the Ali-M3 confidence (0.78, 95% CI, 0.71 to 0.83) in the test set.
The A-blood model, a COVID-19 diagnostic model developed in this study, combines machine-learning and CT evaluation with blood test data and is better than the Ali-M3 framework existing for this purpose. This would significantly aid physicians in making a quicker diagnosis of COVID-19.