Automatic Calculation of Cardiometric Coefficients on Chest X-Ray Images

Abstract

Chest radiography is an indispensable diagnostic method for detecting a variety of medical conditions, such as infections, tumors, injuries, etc. Millions of chest X-ray examinations are conducted annually, providing crucial information about the functioning of the respiratory and circulatory systems. The conventional approach to quantifying cardiothoracic indices, such as the Lupi and Moore indices and the Cardiothoracic Index (CTI), requires considerable time and effort from radiologists. Consequently, it calls for the exploration of computational methods for improvement through deep learning. In this study, we addressed the challenge of automating the calculation of these cardiometric indices. We engaged four experienced radiologists to manually label 800 chest X-ray images each. Using these labeled images, we trained a deep learning model that achieved the level of performance comparable to that of a professional radiologist. Additionally, we have replaced the central points of the indices with landmarks based on the vertebrae, improving the accuracy. The use of AI led to improved accuracy of correct predictions, increasing it from 85.94% to 87.34% for the MOORE coefficient and from 87.55% to 90.67% for the LUPI coefficient.

Keywords

• Artificial intelligence
• cardiometry
• chest X-rays
• deep learning
• spine