Selektive interne Radiotherapie bei fortgeschrittenen Lebertumoren und Metastasen

Hoffmann, RT; Tf, Jakobs; Tatsch, Klaus; Reiser, Morton F.

doi:10.1055/s-0028-1091244

Cited by 2 publications

(2 citation statements)

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“…In the future, the standardized values could also be used in combination with the DICOM image data set for the automatic calculation of surfacenormalized organ volumes. Standardized volume data, such as the expected residual volume of the liver, could make a decisive contribution to therapy in the planning phase, e. g. before oncological liver surgery, selective radioembolization or liver transplants [20][21][22][23]. Automatic implementation of the collected parameters in a digital patient file could also ensure rapid availability of relevant data, such as during follow-up examinations in other institutions or when determining the dose of medication in emergency situations without the possibility of patient history or measurement, for example before lysis therapy for stroke patients [12].…”

Section: Discussionmentioning

confidence: 99%

“…B. das zu erwartende Restvolumen der Leber, könnten in der Planungsphase, z. B. vor onkologischer Leberchirurgie, selektiver Radioembolisation oder vor Lebertransplantationen, entscheidend zur Therapiefindung beitragen [20][21][22][23]. Die automatische Implementierung der erhobenen Parameter in eine digitale Patientenakte könnte zudem die schnelle Verfügbarkeit relevanter Daten, z.…”

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Personalized computed tomography – Automated estimation of height and weight of a simulated digital twin using a 3D camera and artificial intelligence

Geissler

Heiß

Kopp

et al. 2020

Rofo

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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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