Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data

Total body weight estimation by 3D camera systems: Potential high-tech solutions for emergency medicine applications? A scoping review

Background

Weight estimation is required in adult patients when weight-based medication must be administered during emergency care, as measuring weight is often not possible. Inaccurate estimations may lead to inaccurate drug dosing, which may cause patient harm. High-tech 3D camera systems driven by artificial intelligence might be the solution to this problem. The aim of this review was to describe and evaluate the published literature on 3D camera weight estimation methods.

Methods

A systematic literature search was performed for articles that studied the use of 3D camera systems for weight estimation in adults. Data on the study characteristics, the quality of the studies, the 3D camera methods evaluated, and the accuracy of the systems were extracted and evaluated.

Results

A total of 14 studies were included, published from 2012 to 2024. Most studies used Microsoft Kinect cameras, with various analytical approaches to weight estimation. The 3D camera systems often achieved a P10 of 90% (90% of estimates within 10% of actual weight), with all systems exceeding a P10 of 78%. The studies highlighted a significant potential for 3D camera systems to be suitable for use in emergency care.

Conclusion

The 3D camera systems offer a promising method for weight estimation in emergency settings, potentially improving drug dosing accuracy and patient safety. Weight estimates were satisfactorily accurate, often exceeding the reported accuracy of existing weight estimation methods. Importantly, 3D camera systems possess characteristics that could make them very appropriate for use during emergency care. Future research should focus on developing and validating this methodology in larger studies with true external and clinical validation.

Automated Patient Registration in Magnetic Resonance Imaging Using Deep Learning-Based Height and Weight Estimation with 3D Camera: A Feasibility Study

RATIONALE AND OBJECTIVES

Accurate and efficient estimation of patient height and weight is crucial to ensure patient safety and optimize the quality of magnetic resonance imaging (MRI) procedures. Several height and weight estimation methods have been proposed for use in adult patient management, but none is widely established. Estimation by the medical technologists for radiology (MTR) based on personal experience remains to be the most common method. This study aimed to compare a novel deep learning (DL)-based 3-dimensional (3D) camera estimation method to MTR staff in terms of estimation accuracy.

METHODS

A retrospective study was conducted to compare the accuracy of height and weight estimation with a DL-based 3D camera algorithm to the accuracy of height and weight estimation by the MTR. Depth images of the patients were captured during the regular imaging workflow on a low field 0.55 T MRI scanner (MAGNETOM Free.Max, Siemens Healthineers, Erlangen, Germany) and then processed retrospectively. Depth images of a total of 161 patients were used to validate the accuracy of the height and weight estimation algorithm. The accuracy of each estimation method was evaluated by computing the proportions of the estimates within 5% and 15% of actual height (PH05, PH15) and within 10% and 20% of actual weight (PW10, PW20). An acceptable accuracy for height estimation was predetermined to be PH05 = 95% and PH15 = 99% and an acceptable accuracy for weight estimation was predetermined to be PW10 = 70% and PW20 = 95%. The bias in height and weight estimation was measured by the mean absolute percentage error (MAPE).

RESULTS

The retrospective study included 161 adult patients. For 148/161 patients complying with inclusion criteria, DL-based 3D camera algorithm outperformed the MTR in estimating the patient's height and weight in term of accuracy (3D camera: PH05 =98.6%, PH15 =100%, PW10 =85.1%, PW20 =95.9%; MTR: PH05 =92.5%, PH15 =100%, PW10 =75.0%, PW20 =93.2%). MTR had a slightly higher bias in their estimates compared to the DL-based 3D camera algorithm (3D camera: MAPE height=1.8%, MAPE weight=5.6%, MTR: MAPE height=2.2%, MAPE weight=7.5%) CONCLUSION: This study has demonstrated that the estimation of the patient's height and weight by a DL-based 3D camera algorithm is accurate and robust. It has the potential to complement the regular MRI workflows, by providing further automation during patient registration.

The accuracy of total body weight estimation in adults - A systematic review and meta-analysis

BACKGROUND

Weight estimation is required in adult patients when weight-based medication must be administered during emergency care, as measuring weight is often impossible. Inaccurate estimations may lead to inaccurate drug doses, which may cause patient harm. Little is known about the relative accuracy of different methods of weight estimation that could be used during resuscitative care. The aim of this study was to evaluate the performance and suitability of existing weight estimation methods for use in adult emergency care.

METHODS

A systematic literature search was performed for suitable articles that studied the accuracy of weight estimation systems in adults. The study characteristics, the quality of the studies, the weight estimation methods evaluated, the accuracy data, and any information on the ease-of-use of the method were extracted and evaluated.

RESULTS

A total of 95 studies were included, in which 27 different methods of total body weight estimation were described, with 42 studies included in the meta-analysis. The most accurate methods, determined from the pooled estimates of accuracy (the percentage of estimates within 10% of true weight, with 95% confidence intervals) were 3-D camera estimates (88.8% (85.8 to 91.8%)), patient self-estimates (88.7% (87.7 to 89.7%)), the Lorenz method (77.5% (76.4 to 78.6%)) and family estimates (75.0% (71.5 to 78.6%)). However, no method was without significant potential limitations to use during emergency care.

CONCLUSION

Patient self-estimations of weight were generally very accurate and should be the method of choice during emergency care, when possible. However, since alternative estimation methods must be available when confused, or otherwise incapacitated, patients are unable to provide an estimate, alternative strategies of weight estimation should also be available.

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

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.