MultimodNet: A Multimodal Deep Learning Model for COPD Staging based on Chest X-Ray and Pulmonary Function Tests

Chronic Obstructive Pulmonary Disease (COPD) represents a major global health challenge, with early and accurate diagnosis being essential for improving patient outcomes. Traditional diagnostic approaches often rely on single-modality assessments, which may not fully capture the complexity of the disease. This research introduces MultimodNet, a deep learning framework that integrates both chest X-ray images and Pulmonary Function Test (PFT) data to provide a more comprehensive classification of COPD stages. By combining structural information from chest X-rays with functional lung metrics like FEV1, FVC, and the FEV1/FVC ratio, MultimodNet aims to enhance the accuracy and reliability of COPD diagnosis. The core strength of MultimodNet lies in its multimodal approach, which allows for the processing and fusion of diverse data types to overcome the limitations of using a single modality. This integration helps the model provide more accurate predictions regarding COPD progression, classifying patients into one of four stages: Initial, Progressive, Complicated, and Critical. These stages reflect the disease's clinical trajectory, from the onset of symptoms through to advanced stages where life-threatening complications may arise. By leveraging both imaging and clinical data, MultimodNet offers a powerful diagnostic tool that can assist healthcare providers in making early and precise decisions, leading to better treatment planning and improved patient care. This framework has the potential to not only enhance diagnostic workflows but also contribute to the broader field of AI-driven healthcare solutions, where multimodal data integration plays a key role in advancing disease detection and management.