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

•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 rigorous validation, the proposed model demonstrated a mean Out-of-Fold (OOF) validation score markedly 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. 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.