Long Short-Term Memory Based Framework for Longitudinal Assessment of COVID-19 Using CT Imaging and Laboratory Data

Automatic longitudinal assessment of the disease progression of coronavirus disease 2019 (COVID-19) is invaluable to ensure timely treatment for severe or critical patients. An artificial intelligence system that combines chest computed tomography (CT) and laboratory examinations may provide a more accurate diagnosis. To explore an artificial intelligence solution to longitudinally assess the condition of COVID-19 using CT imaging and laboratory findings, from January 27, 2020, to April 3, 2020, multiple follow-up examinations of COVID-19 inpatients were retrospectively collected. CT imaging features were automatically extracted using a deep learning method and combined with laboratory tests. The progression sequences were generated with two follow-ups, each of which contained 60 imaging and 24 laboratory features. Pearson’s correlation was conducted to rank the importance of each univariate feature, and multivariate logistic regression was adopted for feature selection. The selected features were used to train a 2-layer long short-term memory network (LSTM) with pulse oxygen saturation (SpO2) as an indicator of disease progression in three classes: alleviated, stable, and aggravated. The performance of models trained on various feature subsets was compared with five-fold cross validation.559 patients with 1734 examinations were collected, and 1450 progression sequences were generated. Of the 559 patients, 262 (46.9%) were male. The mean age of the patients was 60 ± 14 years. The mean hospitalization duration was 31 ± 12 days. Based on the ranking of importance, 26 features from the imaging and laboratory tests were selected, achieving the best accuracy of 0.85 for progression assessment. The comparisons demonstrated that CT features outperformed laboratory features. The best sensitivities for alleviated and aggravated obtained with CT features alone were 0.83 and 0.85, respectively, while laboratory features improved the assessment precision by about 3%. Longitudinal assessment using deep learning with combined features from CT imaging and laboratory tests better predicts the progression of COVID-19 than either of them.