Artificial Intelligence has Similar Performance to Subjective Assessment of Emphysema Severity on Chest CT
Shadi Ebrahimian, Subba R Digumarthy, Bernardo Bizzo, Andrew Primak, Mathis Zimmermann, Mohammad Mahmoud Tarbiah, Mannudeep K Kalra, Keith J Dreyer
Acad Radiol . 2022 Aug;29(8):1189-1195. doi: 10.1016/j.acra.2021.09.007. Epub 2021 Oct 14.
Rationale and objectives: To compare an artificial intelligence (AI)-based prototype and subjective grading for predicting disease severity in patients with emphysema.
Methods: Our IRB approved HIPAA-compliant study included 113 adults (71±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 prototype (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 differentiate 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 differentiate 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.
Read Full Article Here: https://doi.org/10.1016/j.acra.2021.09.007