Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1
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
Status
Conditions
Conditions
Detailed Description
Study Type
Study Type
Contacts and Locations
Study Contact
Study Contact
- Name: Mike Wells, MD, PhD
- Phone Number: (561) 479-6344
- Email: wellsm@health.fau.edu
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
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
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
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
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
Sponsor
Sponsor
Publications and helpful links
General Publications
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 1791994(1)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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