Background: To externally evaluate the first picture archiving communications system (PACS)-integrated artificial intelligence (AI)-based workflow, trained to automatically detect a predefined computed tomography (CT) slice at the third lumbar vertebra (L3) and automatically perform complete image segmentation for analysis of CT body composition and to compare its performance with that of an established semi-automatic segmentation tool regarding speed and accuracy of tissue area calculation. Methods: For fully automatic analysis of body composition with L3 recognition, U-Nets were trained (Visage) and compared with a conventional image segmentation software (TomoVision). Tissue was differentiated into psoas muscle, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Mid-L3 level images from randomly selected DICOM slice files of 20 CT scans acquired with various imaging protocols were segmented with both methods. Results: Success rate of AI-based L3 recognition was 100%. Compared with semi-automatic, fully automatic AI-based image segmentation yielded relative differences of 0.22% and 0.16% for skeletal muscle, 0.47% and 0.49% for psoas muscle, 0.42% and 0.42% for VAT and 0.18% and 0.18% for SAT. AI-based fully automatic segmentation was significantly faster than semi-automatic segmentation (3 ± 0 s vs. 170 ± 40 s, P < 0.001, for User 1 and 152 ± 40 s, P < 0.001, for User 2). Conclusion: Rapid fully automatic AI-based, PACS-integrated assessment of body composition yields identical results without transfer of critical patient data. Additional metabolic information can be inserted into the patient’s image report and offered to the referring clinicians.
