A Review on COPD Prediction using Convolutional Neural Networks: Approaches, Challenges and Future Directions

Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory condition with a profound impact on global health. Given the importance of early detection for managing its progression, Artificial Intelligence (AI)—especially Convolutional Neural Networks (CNNs)—has emerged as a promising tool for predicting COPD using medical imaging and diagnostic data. This review examines the current state of COPD prediction with CNNs, emphasizing the datasets utilized, the challenges encountered, and the potential clinical applications. This study provides a thorough analysis of both public and proprietary datasets commonly used for training and validating CNN models, and it also discusses key limitations related to data collection, annotation, and patient population variability. Additionally, this paper highlights critical challenges such as model interpretability, generalization to diverse populations, and the integration of CNN results into clinical workflows. Ethical considerations, including data privacy and the influence of biased datasets on model accuracy, are also explored. By addressing these gaps and challenges, the review offers recommendations for future research to strengthen the robustness and reliability of CNN-based COPD prediction, paving the way for improved early detection tools in healthcare.