Capturing complexity in pulmonary system modelling.

Area of Science:

• Pulmonary Medicine
• Computational Biology
• Medical Imaging

Background:

• Respiratory diseases pose a growing global health challenge, complicated by heterogeneous patient responses.
• Accurate diagnosis and treatment are hindered by the complexity of upper airway and lung pathologies.
• Computational models have become crucial tools for understanding respiratory function.

Purpose of the Study:

• To review the evolution of computational models for the respiratory system.
• To highlight successes in translating these models into clinical practice.
• To identify challenges in respiratory modeling and the use of functional data.

Main Methods:

• Review of literature on computational modeling of the respiratory system.
• Analysis of the impact of increasing model complexity (geometric, biophysical).
• Examination of advances in medical imaging, functional imaging, biological knowledge, and computational power.

Main Results:

• Increasingly complex models capture patient heterogeneity and predict function across scales.
• Challenges include model solution difficulties and validation, potentially limiting clinical impact.
• Functional imaging data are increasingly used for model parameterization and testing.

Conclusions:

• Computational models are evolving to better represent respiratory system complexity and patient variability.
• Overcoming challenges in model validation and clinical integration is key to maximizing their impact.
• Leveraging advanced data sources is essential for improving respiratory modeling accuracy and utility.