The aim of this study was to evaluate whether a 3-dimensional (3D) camera can outperform highly trained technicians in precision of patient positioning and whether this transforms into a reduction in patient exposure. Materials and Methods: In a single-center study, 3118 patients underwent computer tomography (CT) scans of the chest and/or abdomen on a latest generation single-source CT scanner supported with an automated patient positioning system by 3D camera. One thousand five hundred fifty-seven patients were positioned laser-guided by a highly trained radiographer (camera off ) and 1561 patients with 3D camera (camera on) guidance. Radiation parameters such as effective dose, organ doses, CT dose index, and dose length product were analyzed and compared. Isocenter accuracy and table height were evaluated between the 2 groups. Results: Isocenter positioning was significantly improved with the 3D camera (P < 0.001) as compared with visual laser-guided positioning. Absolute table height differed significantly (P < 0.001), being higher with camera positioning (165.6 ± 16.2 mm) as compared with laser-guided positioning (170.0 ± 20.4 mm). Radiation exposure decreased using the 3D camera as indicated by dose length product (321.1 ± 266.6 mGy•cm; camera off: 342.0 ± 280.7 mGy•cm; P = 0.033), effective dose (3.3 ± 2.7 mSv; camera off: 3.5 ± 2.9; P = 0.053), and CT dose index (6.4 ± 4.3 mGy; camera off: 6.8 ± 4.6 mGy; P = 0.011). Exposure of radiation-sensitive organs such as colon (P = 0.015) and red bone marrow (P = 0.049) were also lower using the camera. Conclusions: The introduction of a 3D camera improves patient positioning in the isocenter of the scanner, which results in a lower and also better balanced dose reduction for the patients.

No abstract

The terms “notifications” and “alerts” for medical exposures are used by several national and international organisations. Recommendations for CT scanners have been published by the American Association of Physicists in Medicine. Some interventional radiology societies as well as national authorities have also published dose notifications for fluoroscopy-guided interventional procedures. Notifications and alerts may also be useful for optimisation and to avoid unintended and accidental exposures. The main interest in using these values for high-dose procedures (CT and interventional) is to optimise imaging procedures, reducing the probability of stochastic effects and avoiding tissue reactions. Alerts in X-ray systems may be considered before procedures (as in CT), during procedures (in some interventional radiology systems), and after procedures, when the patient radiation dose results are known and processed. This review summarises the different uses of notifications and alerts to help in optimisation for CT and for fluoroscopy-guided interventional procedures as well as in the analysis of unintended and accidental medical exposures. The paper also includes cautions in setting the alert values and discusses the benefits of using patient dose management systems for the alerts, their registry and follow-up, and the differences between notifications, alerts, and trigger levels for individual procedures and the terms used for the collective approach, such as diagnostic reference levels. Key Points • Notifications and alerts on patient dose values for computed tomography (CT) and fluoroscopy-guided interventional procedures (FGIP) allow to improve radiation safety and contribute to the avoidance of radiation injuries and unintended and accidental exposures. • Alerts may be established before the imaging procedures (as in CT) or during and after the procedures as for FGIP. • Dose management systems should include notifications and alerts and their registry for the hospital quality programmes.

No abstract

The prediction of radiation exposure is an important tool for the choice of therapy modality and becomes, as a component of patient-informed consent, increasingly important for both surgeon and patient. The final goal is the implementation of a trained and tested machine learning model in a real-time computer system allowing the surgeon and patient to better assess patient’s personal radiation risk. In summary, 995 patients with ureterorenoscopy over a period from May 2016 to December 2019 were included. According to the suggestions based on actual literature evidence, dose area product (DAP) was categorized into ‘low doses’ ≤ 2.8 Gy·cm2 and ‘high doses’ > 2.8 Gy·cm2 for ureterorenoscopy (URS). To forecast the level of radiation exposure during treatment, six different machine learning models were trained, and 10-fold crossvalidated and their model performances evaluated in training and independent test samples. The negative predictive value for low DAP during ureterorenoscopy was 94% (95% CI: 92–96%). Factors influencing the radiation exposure were: age (p = 0.0002), gender (p = 0.011), weight (p < 0.0001), stone size (p < 0.000001), surgeon experience (p = 0.039), number of stones (p = 0.0007), stone density (p = 0.023), use of flexible endoscope (p < 0.0001) and preoperative stone position (p < 0.00001). The machine learning algorithm identified a subgroup of patients of 81% of the total sample, for which highly accurate predictions (94%) were possible allowing the surgeon to assess patient’s personal radiation risk. Patients without prediction (19%), the medical expert can make decisions as usual. Next step will be the implementation of the trained model in real-time computer systems for clinical decision processes in daily practice.