Development and Validation of an AI-Based Model for Heart Delineation in Chest X-Ray Images

This chapter demonstrates the development and validation of a novel artificial intelligence (AI)-based model for automatically delineating the heart in chest X-ray images. Leveraging a dataset of 469 X-rays from the Arizona Pneumonia Detection challenge, the model employs bounding boxes to predict heart location, a critical marker in diagnosing cardiovascular and respiratory conditions. Key advancements include the adaptation of a Resnet 18 architecture modified for single-channel input and optimized to output precise bounding box coordinates. The model is further enhanced by being very careful with the basic pre-processing of images: resizing images to 224x224 and performing corresponding augmentations of bounding boxes in order not to break data integrity. Besides, for image and annotation reading, a custom dataset class is also developed for rigorous normalization and transformation and augmentation protocols in its way to ensure consistency between the training-validation pairs. Extensive data augmentation included random scaling, rotation, and translation further to make sure of model robustness against variability in clinical imaging. The training was performed using mean squared error loss with the Adam Optimizer and reached a total of 50 epochs in training. The results indicate that AI is going to go a long way in contributing to early detection and intervention for cardiac and pulmonary anomalies, hence an improvement in the management and outcome of patients. It is a perfect avenue that opens ways for greater integrations of AI into medical diagnosis, enabling a revolution in traditional imaging techniques that assist clinical decision-making procedures.