GENERATION OF SYNTHETIC CHEST RADIOGRAPHY IMAGES WITH CHRONIC OBSTRUCTIVE PULMONARY DISEASE USING ARTIFICIAL INTELLIGENCE TECHNIQUES.

The implementation of generative models in medicine, particularly in diagnostic imaging of chronic obstructive pulmonary disease (COPD), signifies a significant advancement in the fusion of artificial intelligence with clinical practice. LunGAN, a generative model synthesizing images exhibiting COPD characteristics, is introduced. These models hold the potential to replicate diverse disease scenarios and manifestations, translating clinical data into visual representations.

This study comprises four pivotal phases. The initial phase entails acquiring and processing clinical data from the Pablo Tobón Uribe hospital database. Subsequently, the second phase focuses on a comprehensive analysis of structured and unstructured information to gain a thorough understanding of the pathology. The third phase involves designing and implementing various models to generate images presenting pertinent pathology characteristics. Lastly, an assessment of image quality is conducted to evaluate the resemblance between real and generated images, demonstrating the models’ capability to accurately replicate chest X-rays.

The proposed generative model possesses the capacity to translate clinical data into visual representations, offering training and learning opportunities for medical professionals while simulating a range of disease scenarios and manifestations without an extensive pool of real patients. This advancement points towards a promising future in integrating artificial intelligence with medicine, providing novel perspectives to confront and surmount present challenges in the treatment and diagnosis of lung diseases, with the overarching goal of continually enhancing healthcare quality.