Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 3

NCT ID: NCT06645548

Last Updated: 2025-12-19

Basic Information

• Recruitment Status: WITHDRAWN
• Clinical Phase: NA
• Study Classification: INTERVENTIONAL
• Study Start Date: 2029-07-01
• Study Completion Date: 2030-07-01

Brief Summary

The goal of this randomized controlled clinical trial is to compare standard methods of weight estimation and drug dose calculations against weight estimates and dose calculations using a 3D camera weight estimation system in critically ill or injured cohorts of patients presenting to the Emergency Department. The main question[s] it aims to answer are:

Are weight estimates from a 3D camera system more accurate than standard methods of weight estimation? Do patients who receive weight estimates with a 3D camera system have fewer drug dosing errors than patients receiving standard care?

Participants will either receive a weight estimate using a 3D camera system, or standard methods of care.

Researchers will compare the 3D camera group to those with standard care to see if the weight estimates are more accurate, to see if drug dosing is more accurate, and to compare the incidence of adverse events related to medications in each group.

Detailed Description

Drug dosing errors can have a catastrophic effect in acutely ill patients such as stroke patients needing thrombolytic therapy or patients requiring urgent sedation. In an acutely ill patient, inaccurate weight estimates are a significant cause of dosing errors, and weight estimates that deviate by >10% from actual weight could make treatment itself life threatening. Inaccurate weight estimates lead to inaccurate drug doses, which can result in potentially fatal treatment failure (from subtherapeutic doses) or potentially fatal adverse events (from supratherapeutic doses). Nearly 75% of treatment failures in obese patients may be related to errors in weight estimation. When clinical care is time-sensitive, it may be impossible to obtain a measured weight in >50% of patients. In these circumstances, a rapid, accurate method for estimating weight is critical. One recent innovation is the use of a low-cost 3D camera system to estimate weight. The 3D camera device (e.g., Intel RealSense D415) is used to obtain a point cloud map of the patient's body, from which a weight estimate can be estimated based on algorithms derived using convoluted neural network analysis. Initial studies have been extremely promising in terms of the accuracy achievable by this system in estimating Total Body Weight (TBW).

The primary aim of this study is to measure the accuracy of weight estimations by the 3D camera system in acutely ill or injured ED patients and compare this accuracy against that of standard care. The researchers will compare the performance and downstream effects of weight estimation using the 3D camera system against standard care in a randomized controlled trial of acutely ill or injured adults presenting to the ED.

The key hypothesis is that the 3D camera system will provide real-time estimates of TBW, IBW and LBW in an emergency setting and will exceed the accuracy of existing methods of weight estimation.

Supporting non-clinical trial studies will establish the accuracy of the 3D camera system in laboratory conditions, and in simulated medical emergencies. However, its performance, and its impact on downstream drug dosing accuracy, needs to be established during emergency care in a real clinical setting. This study will provide an essential perspective about the accuracy and functioning of the 3D camera system as well as real-world weight estimation during emergency care. It will also describe the ability to measure weight using in-bed scales and to obtain weight estimations from patients themselves and family members in ED patients. The secondary objective, to determine the accuracy of drug doses in each arm of the study, will provide critical information on the need for alternative weight scalars in obese and morbidly obese patients presenting to the ED. The study will establish the need for standards and policies to guide dose scaling in obese patients in the ED. Information on the actual usage of drugs that should be scaled to TBW and those that should be scaled to LBW will provide useful real-world insight into the magnitude of the problem in the threat to patient safety by using a "one size fits all" approach to drug dose calculations for all patients, irrespective of weight status.

Acutely ill patients presenting to the ED of a large regional hospital, and who require weight-based drug therapy, will be enrolled in the study. They will be randomised to either receive a weight estimation using a 3D camera system (which will provide estimates of TBW, IBW and LBW), or to receive standard care. All other interventions and medical care will be standard care.

These patients will be followed for the first 72 hours of their hospital stay. The accuracy of the weight estimates will be compared between the groups, as will the drug dose accuracy, and any adverse events related to drug therapy.

Conditions

Body Weights and Measures; Body Weight in the Overweight and Obese Class - I Population; Drug Dose; Weight Estimate

Keywords

Weight estimation; Emergency drug dose; 3D camera weight estimate; computer vision

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: DOUBLE

Study Groups

Control arm (standard care)

TBW, LBW and IBW will be estimated using standard care processes. All relevant medical care will be based on this weight for the first 72 hours

Group Type: NO_INTERVENTION

No interventions assigned to this group

3D camera weight estimation

TBW, LBW and IBW will be automatically estimated using the 3D camera system. All relevant medical care will be based on this weight for the first 72 hours

Group Type: EXPERIMENTAL

Weight estimation using 3D camera

Intervention Type: OTHER

Total body weight, ideal body weight and lean body weight estimates will be obtained using a 3D camera system. This weight will be used for calculation of weight-based drug doses and other weight-based interventions.

Interventions

Weight estimation using 3D camera

Total body weight, ideal body weight and lean body weight estimates will be obtained using a 3D camera system. This weight will be used for calculation of weight-based drug doses and other weight-based interventions.

Intervention Type: OTHER

Other Intervention Names

Weight estimation using computer vision

Eligibility Criteria

Inclusion Criteria

• Any patient presenting to the Emergency Department of the study site, who will require any form of weight-based intravenous drug therapy, and who will be admitted to the hospital.

Exclusion Criteria

• Patients who are unable to provide consent.
• Patients whose medical treatment could be negatively impacted by participation in the study.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Florida Atlantic University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

References

Wells M, Goldstein LN, Cattermole G. Development and validation of a length- and habitus-based method of total body weight estimation in adults. Am J Emerg Med. 2022 Mar;53:44-53. doi: 10.1016/j.ajem.2021.12.053. Epub 2021 Dec 28.

Reference Type: BACKGROUND

PMID: 34974251 (View on PubMed)

Wells M, Goldstein LN. Estimating Lean Body Weight in Adults With the PAWPER XL-MAC Tape Using Actual Measured Weight as an Input Variable. Cureus. 2022 Sep 17;14(9):e29278. doi: 10.7759/cureus.29278. eCollection 2022 Sep.

Reference Type: BACKGROUND

PMID: 36277563 (View on PubMed)

Wells M, Goldstein LN, Cattermole G. Development and Validation of a Length- and Habitus-Based Method of Ideal and Lean Body Weight Estimation for Adults Requiring Urgent Weight-Based Medical Intervention. Eur J Drug Metab Pharmacokinet. 2022 Nov;47(6):841-853. doi: 10.1007/s13318-022-00796-3. Epub 2022 Sep 19.

Reference Type: BACKGROUND

PMID: 36123560 (View on PubMed)

Wells M, Goldstein L. Appropriate Statistical Analysis and Data Reporting for Weight Estimation Studies. Pediatr Emerg Care. 2023 Jan 1;39(1):62-63. doi: 10.1097/PEC.0000000000002862. Epub 2022 Oct 1. No abstract available.

Reference Type: BACKGROUND

PMID: 36190388 (View on PubMed)

Wells M, Goldstein LN, Alter SM, Solano JJ, Engstrom G, Shih RD. The accuracy of total body weight estimation in adults - A systematic review and meta-analysis. Am J Emerg Med. 2024 Feb;76:123-135. doi: 10.1016/j.ajem.2023.11.037. Epub 2023 Nov 29.

Reference Type: BACKGROUND

PMID: 38056057 (View on PubMed)

Sonar VG, Jan MT, Wells M, Pandya A, Engstrom G, Shih R, Furht B. Estimating Body Volume and Height Using 3D Data. arxiv. 2024 September; 2410.02800

Reference Type: BACKGROUND

Jan MT, Kumar A, Wells M, Pandya A, Engstrom G, Shih R, Furht B. Comprehensive Survey of Body Weight Estimation: Techniques, Datasets and Applications. Multimedia Tools and Applications. 2024 October.

Reference Type: BACKGROUND

Wells M, Goldstein LN, Wells T, Ghazi N, Pandya A, Furht B, Engstrom G, Jan MT, Shih R. Total body weight estimation by 3D camera systems: Potential high-tech solutions for emergency medicine applications? A scoping review. J Am Coll Emerg Physicians Open. 2024 Oct 4;5(5):e13320. doi: 10.1002/emp2.13320. eCollection 2024 Oct.

Reference Type: BACKGROUND

PMID: 39371964 (View on PubMed)

Other Identifiers

1791994(3)

Identifier Type: -

Identifier Source: org_study_id