Fig. 4: Performance of the method for detecting BOS at different stages of the disease.
From: Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT
Left: aggregated weighted ROC curves representing performance of the deep neural network in distinguishing thoracic CT scans of BOS patients at different stages of the disease from scans of patients that were not diagnosed with BOS. Right: ROC-AUC values for individual splits. The box extends from the first quartile (Q1) to the third quartile (Q3) of the data, with a line at the median. The whiskers extend from the box to the farthest data point lying within 1.5x the interquartile range (IQR) from the box. Flier points are those past the end of the whiskers. The number of data points for each box plot is 5 (n = 5). BOS bronchiolitis obliterans syndrome.
