Rofo 2021; 193(04): 437-445
DOI: 10.1055/a-1253-8558
Quality/Quality Assurance

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

Article in several languages: English | deutsch
Frederik Geissler
1  Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
2  Department of Radiology, University Hospital Erlangen, Erlangen, Germany
,
Rafael Heiß
2  Department of Radiology, University Hospital Erlangen, Erlangen, Germany
,
Markus Kopp
2  Department of Radiology, University Hospital Erlangen, Erlangen, Germany
,
Marco Wiesmüller
2  Department of Radiology, University Hospital Erlangen, Erlangen, Germany
,
Marc Saake
1  Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
2  Department of Radiology, University Hospital Erlangen, Erlangen, Germany
,
Wolfgang Wuest
1  Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
2  Department of Radiology, University Hospital Erlangen, Erlangen, Germany
3  Imaging Science Institute, Erlangen, Germany
,
Andreas Wimmer
4  Siemens Healthcare GmbH, Forchheim, Germany
,
Veronika Prell
4  Siemens Healthcare GmbH, Forchheim, Germany
,
Michael Uder
1  Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
2  Department of Radiology, University Hospital Erlangen, Erlangen, Germany
3  Imaging Science Institute, Erlangen, Germany
,
Matthias Stefan May
1  Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
2  Department of Radiology, University Hospital Erlangen, Erlangen, Germany
3  Imaging Science Institute, Erlangen, Germany
› Author Affiliations

Abstract

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:

  • Machine learning can calculate patient-avatars from 3D camera acquisitions.

  • Height and weight of the digital twins are comparable to real measurements of the patients.

  • Estimations by medical staff are less precise.

  • The values can be used for calculation of contrast agent dosage.

Citation Format

  • Geissler F, Heiß R, Kopp M et al. Personalized computed tomography – Automated estimation of height and weight of a simulated digital twin using a 3D camera and artificial intelligence. Fortschr Röntgenstr 2021; 193: 437 – 445



Publication History

Received: 05 May 2020

Accepted: 26 August 2020

Publication Date:
03 November 2020 (online)

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