The workflow of computed tomography (CT)-based artificial intelligence applied in chronic obstructive pulmonary disease (COPD) management. Features of emphysema and bronchial tree will be extracted by segmenting the CT scan and used for model training with several network choices. Finally, the model will combine clinical data to output the results of COPD classification, diagnosis, and prognosis.

Chronic obstructive pulmonary disease (COPD) stands as a global health crisis, responsible for substantial morbidity and mortality on a worldwide scale. Its insidious nature underscores the importance of early detection and accurate diagnosis. While spirometry has been the cornerstone for COPD diagnosis, the role of computed tomography (CT) imaging has evolved, offering a valuable avenue for early detection and subtype classification. Recently, the advent of artificial intelligence (AI) has brought forth the potential to revolutionize the accuracy and efficiency of COPD diagnosis, with a specific focus on CT images. This intersection of healthcare and technology signifies a paradigm shift in the way we approach COPD management. The transformative capacity of AI positions it as a vital instrument for early detection and precise subtype classification of COPD. Moreover, the synergistic relationship between medical imaging and AI paves the way for more precise and efficient disease management. Therefore, in this perspective, we tend to offer a comprehensive exploration of the latest breakthroughs in the field of CT-based AI in COPD diagnosis, aiming to demonstrate the promise and potential of AI in refining the accuracy of COPD classification and to illuminate the evolving landscape of AI's impact on COPD management.

Article Access: https://doi.org/10.1002/mef2.73

More about MedComm-Future Medicine: https://onlinelibrary.wiley.com/journal/27696456

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