PulmoClass-3DAtt: A Self-Attention Network for Classification of COPD, PRISm and Normal.

Background: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, making its accurate diagnosis critical. It is difficult to distinguish COPD from preserved ratio impaired spirometry (PRISm), due to their shared small-airway pathology. This study develops a novel deep-learning framework that combines chest CT images with clinical variables to discriminate between COPD, PRISm, and normal categories. Methods: In this retrospective study, consecutive subjects were enrolled from a university-affiliated tertiary hospital between January 2018 and June 2024. After random split at an 8:2 ratio, the training cohort was used to develop a convolutional encoder that extracts imaging features and simultaneously predicts five spirometric parameters (FEV Results: The cohort comprised 1918 participants (1362 COPD, 174 PRISm, 382 normal controls). The model achieved robust overall performance (AUC = 0.86, ACC = 0.87). For COPD, Normal and PRISm the respective values are ACC 0.87, 0.88, 0.87; Se 0.90, 0.88, 0.42; Sp 0.84, 0.88, 0.97; Pre 0.85, 0.79, 0.72; and F1-score 0.88, 0.85, 0.53. In the validation set, the five spirometric metrics were predicted with MSE 0.03-0.35, MAE 0.14-0.46 and CCC 0.56-0.83. Conclusion: By integrating multimodal imaging-function data through a self-attention framework, PulmoClass-3DAtt reliably discriminates among COPD, PRISm and normal status, providing an immediately applicable tool for clinical decision support and the delivery of precision pulmonary medicine.