First PACS‐integrated artificial intelligence‐based software tool for rapid and fully automatic analysis of body composition from CT in clinical routine

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 se...

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Bibliographic Details
Published in:JCSM clinical reports 2022-01, Vol.7 (1), p.3-11
Main Authors: Beetz, Nick Lasse, Maier, Christoph, Segger, Laura, Shnayien, Seyd, Trippel, Tobias Daniel, Lindow, Norbert, Bousabarah, Khaled, Westerhoff, Malte, Fehrenbach, Uli, Geisel, Dominik
Format: Article
Language:English
Subjects:
Abdomen
AI
Image segmentation
Artificial intelligence
Body composition
Body fat
Body mass index
Cardiovascular disease
Computed tomography
CT
Datasets
Integrated software
Measurement techniques
Medical imaging
Musculoskeletal system
Neural networks
Obesity
Older people
Patients
Physical fitness
Radiation
Sarcopenia
Sarcopenic obesity
Vertebrae
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Summary: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 
ISSN:2521-3555
2521-3555
DOI:10.1002/crt2.44