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Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1

NCT ID: NCT06646120

Last Updated: 2025-12-19

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

WITHDRAWN

Study Classification

OBSERVATIONAL

Study Start Date

2025-07-01

Study Completion Date

2026-06-30

Brief Summary

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The goal of this observational study is to train and validate an AI-driven 3D camera system to estimate total body weight, ideal body weight and lean body weight in male and female adult volunteers of all ages. The main questions this study aims to answer are:

* What degree of accuracy of weight estimation can we achieve with an AI-driven 3D camera weight estimation system?
* Is this accuracy the same in adults of both sexes, all ages, and all body types (underweight, normal weight, overweight)? Participants will undergo some anthropometric measurements (height, mid-arm circumference, weight circumference, hip circumference, measured weight), a DXA scan (to measure lean body weight), and 3D imaging using a 3D camera.

There will be no interventions.

Detailed Description

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This study is a single-centre observational study to train, internally validate, and test an AI-driven 3D camera weight estimation system. Our hypothesis is that this system, when used in the management of acutely ill patients, will be able to estimate total body weight, ideal body weight, and lean body weight more accurately than other current point-of-care system. Healthy volunteers will be used to train and test the system. During a single data collection session of approximately 30 minutes, baseline anthropometric data, a DXA scan, and 3D camera images of volunteers lying on a medical stretcher will be captured. There will be no interventions, and no follow up of participants. The collected data will be used to train an AI algorithm (based on artificial neural networks) to estimate weight using a single depth image. Once the AI system is fully evolved, the accuracy of its weight estimation performance will be evaluated in an independent test dataset.

Conditions

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Body Weights and Measures Body Weight in the Overweight and Obese Class - I Population

Keywords

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3D camera weight estimation computer vision weight estimation

Study Design

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Observational Model Type

OTHER

Study Time Perspective

CROSS_SECTIONAL

Eligibility Criteria

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Inclusion Criteria

* Any willing volunteer.

Exclusion Criteria

* Participants with a body weight exceeding the DXA machine capacity \>204kg (450lbs);
* Pregnant participants;
* Participants with medical conditions that could confound the study;
* Participants with any metallic surgical implants;
* Participants who have had an x-ray with contrast in the past week;
* Participants who have taken calcium supplements in the 24 hours prior to the study.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Florida Atlantic University

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

References

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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 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, 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 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, 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)

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

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1791994(1)

Identifier Type: -

Identifier Source: org_study_id