This article discusses the use of artificial intelligence (AI) to optimize the quality of cardiac ultrasound images. The study used a public database of ultrasound images called CAMUS and developed a method called UltraGAN, which uses AI to improve image quality. The method involves training a convolutional neural network (CNN) with high-quality image features and transferring this information to medium and low-quality images. The results showed that UltraGAN can automatically identify structures in ultrasounds with over 90% accuracy without compromising the anatomical characteristics of the original images. This research demonstrates the potential of AI as a complementary tool in screening for congenital heart diseases.