(1) To explore the potential impact of an AI dual-energy CT (DECT) prototype on decision making and workflows by investigating its capabilities to differentiate COVID-19 from immunotherapy-related pneumonitis. (2) Methods: From 3 April 2020 to 12 February 2021, DECT from biometrically matching patients with COVID-19, pneumonitis, and inconspicuous findings were selected from our clinical routine. Three blinded readers independently scored each pulmonary lobe analogous to CO-RADS. Inter-rater agreement was determined with an intraclass correlation coefficient (ICC). Averaged perfusion metrics per lobe (iodine uptake in mg, volume without vessels in ml, iodine concentration in mg/mL) were extracted using manual segmentation and an AI DECT prototype. A generalized linear mixed model was used to investigate metric validity and potential distinctions at equal CO-RADS scores. Multinomial regression measured the contribution “Reader”, “CO-RADS score”, and “perfusion metrics” to diagnosis. The time to diagnosis was measured for manual vs. AI segmentation. (3) Results: We included 105 patients (62 ± 13 years, mean BMI 27 ± 2). There were no significant differences between manually and AI-extracted perfusion metrics ( p = 0.999). Regardless of the CO-RADS score, iodine uptake and concentration per lobe were significantly higher in COVID-19 than in pneumonitis ( p < 0.001). In regression, iodine uptake had a greater contribution to diagnosis than CO-RADS scoring (Odds Ratio (OR) = 1.82 [95%CI 1.10–2.99] vs. OR = 0.20 [95%CI 0.14–0.29]). The AI prototype extracted the relevant perfusion metrics significantly faster than radiologists (10 ± 1 vs. 15 ± 2 min, p < 0.001). (4) Conclusions: The investigated AI prototype positively impacts decision making and workflows by extracting perfusion metrics that differentiate COVID-19 from visually similar pneumonitis significantly faster than radiologists.
【저자키워드】 COVID-19, artificial intelligence, tomography, X-ray computed, dual energy, 【초록키워드】 Diagnosis, Impact, Validity, BMI, Concentration, Volume, Radiologists, Metrics, Intraclass correlation coefficient, no significant difference, radiologist, linear mixed model, ICC, vessel, was measured, greater, selected, was used, blinded, significantly, investigated, was determined, faster, significantly higher, Ratio, 105 patient, patients with COVID-19, pulmonary lobe, scored, 【제목키워드】 Segmentation, Pneumonitis, Perfusion, finding, Can, Infiltrate,