Advancing idiopathic pulmonary fibrosis prognosis through integrated CNN-LSTM predictive modeling and uncertainty quantification

Abstract

In the realm of pulmonary medicine, prognostic assessment of Idiopathic Pulmonary Fibrosis (IPF) poses a significant challenge, necessitating advancements in predictive analytics. This study introduces a pioneering predictive model that synergistically utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze CT scans and clinical data, offering a dynamic prediction of IPF progression. The model’s methodology is rooted in the integration of detailed spatial features and temporal clinical patterns to forecast disease trajectory with heightened accuracy. A novel aspect of this model is its built-in mechanism for uncertainty quantification, enhancing the interpretability of its prognostic output. Through validation, the proposed model demonstrated a mean Out-of-Fold (OOF) validation score 6.9679 and accuracy of 95% notably superior to traditional methods, alongside a conservative yet precise approach to uncertainty estimation. The findings represent a substantial improvement in predictive capabilities, emphasizing the model’s potential to inform clinical decision-making and facilitate personalized treatment strategies. The implications of this research extend beyond IPF, providing a framework for future explorations into machine learning applications in complex disease prognostics.