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

December 15, 2025 updated by: Florida Atlantic University

Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1 - Establish a Model Using a Single 3D Camera Image of a Supine Patient to Accurately Estimate TBW, IBW And LBW.

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.

Study Overview

Status

Withdrawn

Conditions

Detailed Description

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.

Study Type

Observational

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Students, staff and faculty at the Boca Raton campus of Florida Atlantic University.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
TBW estimation
Time Frame: Baseline
Accuracy of TBW estimation using 3D camera system
Baseline
IBW estimation
Time Frame: Baseline
Accuracy of IBW estimation using 3D camera system
Baseline
LBW estimation
Time Frame: Baseline
Accuracy of LBW estimation using 3D camera system
Baseline

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sex-related accuracy
Time Frame: Baseline
Difference in accuracy between males and females
Baseline
Age-related accuracy
Time Frame: Baseline
Accuracy of weight estimation by age-group
Baseline
BMI-related accuracy
Time Frame: Baseline
Accuracy of weight estimation by subgroup of weight status
Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Florida Atlantic University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

July 1, 2025

Primary Completion (Estimated)

June 30, 2026

Study Completion (Estimated)

June 30, 2026

Study Registration Dates

First Submitted

October 15, 2024

First Submitted That Met QC Criteria

October 16, 2024

First Posted (Actual)

October 17, 2024

Study Record Updates

Last Update Posted (Actual)

December 19, 2025

Last Update Submitted That Met QC Criteria

December 15, 2025

Last Verified

December 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 1791994(1)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

Cloud point data of 3D images will be shared, on request.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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