Impact of the use of deep-learning 3D camera on workflow and patient positioning in CT examinations

This study aims to assess the impact of the use of deep-learning three-dimensional (3D) camera on workflow and patient positioning in CT examinations in comparison with manual positioning by radiographers.

This prospective study analyzed 596 participants (median age, 71 years; 364 men) who underwent unenhanced chest-abdomen-pelvis CT between October 2023 and January 2024 by either manual positioning (manual group) or automatic positioning using a deep-learning 3D camera (camera group). The scan parameters used in the two groups were completely matched. The CT room time, from entering to leaving the CT scan room, was compared between the two groups. Moreover, the off-center distance, which is the difference between the patient and scanner's isocenters, CT dose-index volume (CTDI_vol), dose-length product (DLP), and standard deviation (SD) of the CT attenuation of the abdominal aorta at the midpoint of the scan range as background noise were compared between the two groups.

The median CT room time was shorter in the camera group than in the manual group (223 s vs. 255 s; P < 0.001). No difference in the median off-center distance (14 mm vs. 13 mm; P = 0.30), CTDI_vol (5 mGy vs. 6 mGy; P = 0.58), DLP (410 mGy*cm vs. 416 mGy*cm; P = 0.64), and background noise (9 HU vs. 9 HU; P = 0.19) was observed between the manual and camera groups.

Patient positioning with a deep-learning 3D camera improved the workflow of CT examinations compared with manual positioning by radiographers. However, its impacts on off-center distance, CTDI_vol, DLP, and background noise were minimal.