Advancements in the Use of Artificial Intelligence for Early Diagnosis of Chronic Obstructive Pulmonary Disease Using Chest X-rays and CT Imaging
Keywords:
Chronic Obstructive Pulmonary Disease (COPD), Artificial Intelligence (AI), Deep Learning, Chest X-ray, CT Imaging, Early Diagnosis, Convolutional Neural Networks (CNNs), Radiomics, Medical ImagingAbstract
Chronic Obstructive Pulmonary Disease (COPD) remains a leading cause of morbidity and mortality worldwide, with early detection being crucial for effective intervention. Recent advancements in artificial intelligence (AI) have demonstrated significant promise in automating and enhancing the accuracy of COPD diagnosis using chest imaging modalities such as X-rays and computed tomography (CT) scans. This paper reviews and synthesizes the latest developments in AI-assisted diagnosis of COPD, emphasizing model accuracy, interpretability, and integration into clinical workflows. Drawing from original research, we present comparative analyses of convolutional neural networks (CNNs), ensemble models, and radiomics-based approaches. Furthermore, we include representative imaging data to contextualize these findings. Our review reveals that AI models trained on large, diverse imaging datasets can outperform traditional diagnostic methods, particularly in the early stages of COPD, thereby presenting a transformative potential for pulmonary healthcare systems.
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