The aim of this study was to evaluate the performance of a tablet-based, digitized structured self-assessment (DSSA) of patient anamnesis (PA) prior to computed tomography (CT). Of the 317 patients consecutively referred for CT, the majority (n = 294) was able to complete the tablet-based questionnaire, which consisted of 67 items covering social anamnesis, lifestyle factors (e.g., tobacco abuse), medical history (e.g., kidney diseases), current symptoms, and the usability of the system. Patients were able to mark unclear questions for a subsequent discussion with the radiologist. Critical issues for the CT examination were structured and automatically highlighted as “red flags” (RFs) in order to improve patient interaction. RFs and marked questions were highly prevalent (69.5% and 26%). Missing creatinine values (33.3%), kidney diseases (14.4%), thyroid diseases (10.6%), metformin (5.5%), claustrophobia (4.1%), allergic reactions to contrast agents (2.4%), and pathological TSH values (2.0%) were highlighted most frequently as RFs. Patient feedback regarding the comprehensibility of the questionnaire and the tablet usability was mainly positive (90.9%; 86.2%). With advanced age, however, patients provided more negative feedback for both (p = 0.007; p = 0.039). The time effort was less than 20 min for 85.1% of patients, and faster patients were significantly younger (p = 0.046). Overall, the DSSA of PA prior to CT shows a high success rate and is well accepted by most patients. RFs and marked questions were common and helped to focus patients’ interactions and reporting towards decisive aspects.

Purpose The aim of this study was to develop an algorithm for automated estimation of patient height and weight during computed tomography (CT) and to evaluate its accuracy in everyday clinical practice. Materials and methods Depth images of 200 patients were recorded with a 3D camera mounted above the patient table of a CT scanner. Reference values were obtained using a calibrated scale and a measuring tape to train a machine learning algorithm that fits a patient avatar into the recorded patient surface data. The resulting algorithm was prospectively used on 101 patients in clinical practice and the results were compared to the reference values and to estimates by the patient himself, the radiographer and the radiologist. The body mass index was calculated from the collected values for each patient using the WHO formula. A tolerance level of 5 kg was defined in order to evaluate the impact on weight-dependent contrast agent dosage in abdominal CT. Results Differences between values for height, weight and BMI were non-significant over all assessments (p > 0.83). The most accurate values for weight were obtained from the patient information (R² = 0.99) followed by the automated estimation via 3D camera (R² = 0.89). Estimates by medical staff were considerably less precise (radiologist: R² = 0.78, radiographer: R² = 0.77). A body-weight dependent dosage of contrast agent using the automated estimations matched the dosage using the reference measurements in 65 % of the cases. The dosage based on the medical staff estimates would have matched in 49 % of the cases. Conclusion Automated estimation of height and weight using a digital twin model from 3D camera acquisitions provide a high precision for protocol design in computer tomography. Key points:  Citation Format

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