Predicting Short-Term Outcome of COVID-19 Pneumonia Using Deep Learning-Based Automatic Detection Algorithm Analysis of Serial Chest Radiographs

This study aimed to evaluate short-term clinical outcomes in COVID-19 pneumonia patients using parameters derived from a commercial deep learning-based automatic detection algorithm (DLAD) applied to serial chest radiographs (CXRs). We analyzed 391 patients with COVID-19 who underwent serial CXRs during isolation at a residential treatment center (median interval: 3.57 days; range: 1.73-5.56 days). Patients were categorized into two groups: the improved group (<i>n</i> = 309), who completed the standard 7-day quarantine, and the deteriorated group (<i>n</i> = 82), who showed worsening symptoms, vital signs, or CXR findings. Using DLAD's consolidation probability scores and gradient-weighted class activation mapping (Grad-CAM)-based localization maps, we quantified the consolidation area through heatmap segmentation. The weighted area was calculated as the sum of the consolidation regions' areas, with each area weighted by its corresponding probability score. Change rates (Δ) were defined as per-day differences between consecutive measurements. Prediction models were developed using Cox proportional hazards regression and evaluated daily from day 1 to day 7 after the subsequent CXR acquisition. Among the imaging factors, baseline probability and ΔProbability, ΔArea, and ΔWeighted area were identified as prognostic indicators. The multivariate Cox model incorporating baseline probability and ΔWeighted area demonstrated optimal performance (C-index: 0.75, 95% Confidence Interval: 0.68-0.81; integrated calibration index: 0.03), with time-dependent AUROC (Area Under Receiver Operating Curve) values ranging from 0.74 to 0.78 across daily predictions. These findings suggest that the Δparameters of DLAD can aid in predicting short-term clinical outcomes in patients with COVID-19.