Artificial Intelligence has Similar Performance to Subjective Assessment of Emphysema Severity on Chest CT

Rationale and Objectives: To compare an artificial intelligence (AI)-based prototype and subjective grading for predicting disease sever-ity in patients with emphysema. Methods: Our IRB approved HIPAA-compliant study included 113 adults (71</n>8 years; 47 females, 66 males) who had both non-contrast chest CT and pulmonary function tests performed within a span of 2 months. The disease severity was classified based on the forced expiratory volume in 1 second (FEV1 as % of predicted) into mild, moderate, and severe. 2 thoracic radiologists (RA), blinded to the clinical and AI results, graded severity of emphysema on a 5-point scale suggested by the Fleischner Society for each lobe. The whole lung scores were derived from the summation of lobar scores. Thin-section CT images were processed with the AI-Rad Companion Chest pro-totype (Siemens Healthineers) to quantify low attenuation areas (LAA < -950 HU) in whole lung and each lobe separately. Bronchial abnormality was assessed by both radiologists and a fully automated software (Philips Healthcare). Results: Both AI (AUC of 0.77; 95% CI: 0.68 -0.85) and RA (AUC: 0.76, 95% CI: 0.65 -0.84) emphysema quantification could differenti-ate mild, moderate, and severe disease based on FEV1. There was a strong positive correlation between AI and RA (r = 0.72 -0.80; p <0.001). The combination of emphysema and bronchial abnormality quantification from radiologists' and AI assessment could differenti-ate between different severities with AUC of 0.80 -0.82 and 0.87, respectively. Conclusion: The assessed AI-prototypes can predict the disease severity in patients with emphysema with the same predictive value as the radiologists.