Automatic Cardiothoracic Ratio Calculation With Deep Learning

Deep learning is a growing trend in medical image analysis. There are limited data of deep learning techniques applied in Chest X-rays. This paper proposed a deep learning algorithm for cardiothoracic ratio (CTR) calculation in chest X-rays. A fully convolutional neural network was employed to segment chest X-ray images and calculate CTR. CTR values derived from the deep learning model were compared with the reference standard using Bland-Altman analysis and linear correlation graphs, and intra-class correlation (ICC) analyses. Diagnostic performance of the model for the detection of heart enlargement was assessed and compared with other deep learning methods and radiologists. CTR values derived from the deep learning method showed excellent agreement with the reference standard, with mean difference 0.0004 ± 0.0133, 95% limits of agreement -0.0256 to 0.0264. Correlation coefficient between deep learning and reference standard was 0.965 (P