Impact of intelligent virtual and AI-based automated collimation functionalities on the efficiency of radiographic acquisitions - Rasche, A.; Brader, P.; Borggrefe, J.; Seuß, Hannes; Carr, Z.; Hebecker, A.; ten Cate, G. (Radiography, 2024)
Introduction: Intelligent virtual and AI-based collimation functionalities have the potential to enable an efficient workflow for radiographers, but the specific impact on clinical routines is still unknown. This study analyzes primarily the influence of intelligent collimation functionalities on the examination time and the number of needed interactions with the radiography system. Methods: An observational study was conducted on the use of three camera-based intelligent features at five clinical sites in Europe and the USA: AI-based auto thorax collimation (ATC), smart virtual ortho (SVO) collimation for stitched long-leg and full-spine examinations, and virtual collimation (VC) at the radiography system workstation. Two people conducted semi-structured observations during routine examinations to collect data with the functionalities either activated or deactivated. Results: Median exam duration was 31 vs. 45 s (p < 0.0001) for 95 thorax examinations with ATC and 94 without ATC. For stitched orthopedic examinations, 34 were performed with SVO and 40 without SVO, and the median exam duration was 62 vs. 82 s (p < 0.0001). The median time for setting the ortho range – i.e., the time between setting the upper and the lower limits of the collimation field – was 7 vs. 16 s for 39 examinations with SVO and 43 without SVO (p < 0.0001). In 109 thorax examinations with ATC and 112 without ATC, the median number of system interactions was 1 vs. 2 (p < 0.0001). VC was used to collimate in 2.4% and to check the collimation field in 68.5% of 292 observed chest and other examinations. Conclusion: ATC and SVO enable the radiographer to save time during chest or stitched examinations. Additionally, ATC reduces machine interactions during chest examinations. Implications for practice: System and artificial intelligence can support the radiographer during the image acquisition by providing a more efficient workflow.
CRIS-Team Scopus Importer:2024-05-24
Keywords: Acquisition workflow; Artificial intelligence; Digital radiography; Medical imaging; Radiographer; Workforce