End-to-end Learning of Body Weight Prediction from Point Clouds with Basis Point Sets

Bigalke, Alexander; Hansen, Lasse; Heinrich, Mattias P.

doi:10.1007/978-3-658-33198-6_59

Cited by 2 publications

(7 citation statements)

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“…Pfitzner et al [28] and our prior work [4] already demonstrated that machine learning-based methods are capable of deriving precise weight estimates of lying patients on the basis of point cloud data. Point clouds carry rich geometric information while preserving the patient's data privacy [34] and are thus particularly suitable for the given problem.…”

Section: Introductionmentioning

confidence: 92%

“…The authors start from a volume-based weight estimate in their initial work [26] and gradually include PCA-based features in [27] and contourbased features in [28], which are fed into a neural network for weight regression. Contrary to these feature-based methods, we proposed in our prior work [4] to use basis point sets (BPS) [30] for end-to-end learning of weight estimation from point clouds. All of these methods assume the patient to be fully visible and not to be covered by a blanket.…”

Section: Weight Estimation In Clinical Settingsmentioning

confidence: 99%

“…Whereas the proposed methods predict weight estimates with a promising accuracy, they involve a critical drawback: the methods are designed for and evaluated under highly controlled conditions. Patients are expected to be uncovered and in supine position in [4] and additionally even need to take a specific pose in [28].…”

Section: Introductionmentioning

confidence: 99%

“…Second, it is difficult to distinguish volume that actually belongs to the patient from volume belonging to the blanket and hollow space under the cover. As a consequence, existing methods are either no longer applicable at all [28] or suffer from a substantial degradation of accuracy [4]. Specifically, we evaluated our prior work [4] under occlusions by a blanket and observed an increase of the error of weight estimates by up to 58%.…”

Section: Introductionmentioning

confidence: 99%

“…As a consequence, existing methods are either no longer applicable at all [28] or suffer from a substantial degradation of accuracy [4]. Specifically, we evaluated our prior work [4] under occlusions by a blanket and observed an increase of the error of weight estimates by up to 58%. In practice, the predicted weight estimates will thus either be less accurate or clinical staff needs to manually remove the cover.…”

Section: Introductionmentioning

confidence: 99%

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Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

et al. 2021

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Purpose Body weight is a crucial parameter for patient-specific treatments, particularly in the context of proper drug dosage. Contactless weight estimation from visual sensor data constitutes a promising approach to overcome challenges arising in emergency situations. Machine learning-based methods have recently been shown to perform accurate weight estimation from point cloud data. The proposed methods, however, are designed for controlled conditions in terms of visibility and position of the patient, which limits their practical applicability. In this work, we aim to decouple accurate weight estimation from such specific conditions by predicting the weight of covered patients from voxelized point cloud data. Methods We propose a novel deep learning framework, which comprises two 3D CNN modules solving the given task in two separate steps. First, we train a 3D U-Net to virtually uncover the patient, i.e. to predict the patient’s volumetric surface without a cover. Second, the patient’s weight is predicted from this 3D volume by means of a 3D CNN architecture, which we optimized for weight regression. Results We evaluate our approach on a lying pose dataset (SLP) under two different cover conditions. The proposed framework considerably improves on the baseline model by up to $${16}{\%}$$ 16 % and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to $${52}{\%}$$ 52 % . Conclusion We present a novel pipeline to estimate the weight of patients, which are covered by a blanket. Our approach relaxes the specific conditions that were required for accurate weight estimates by previous contactless methods and thus constitutes an important step towards fully automatic weight estimation in clinical practice.

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Section: Introductionmentioning

confidence: 92%

Section: Weight Estimation In Clinical Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

et al. 2021

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

Wells,

Goldstein,

Wells

et al. 2024

Preprint

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BackgroundWeight 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.MethodsA 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.ResultsA 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.ConclusionThe 3D camera systems offer a promising method for weight estimation in emergency settings, potentially improving drug dosing accuracy and patient safety. Weight estimates were extremely accurate. 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.

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End-to-end Learning of Body Weight Prediction from Point Clouds with Basis Point Sets

Cited by 2 publications

References 7 publications

Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

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

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