Body Weights and MeasuresNCT06646120
Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1
An observational study of Body Weights and Measures and Body Weight in the Overweight and Obese Class - I Population, run by Florida Atlantic University.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Body Weights and Measures, Body Weight in the Overweight and Obese Class - I Population
- How many people
- 0 participants
- Where
- No sites listed yet
- Study dates
- 1 Jul 2025to 30 Jun 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Students, staff and faculty at the Boca Raton campus of Florida Atlantic University.
Who may be able to join
- Any willing volunteer.
Who cannot join
- 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.
About this study
Official title: 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.
Read the 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.
- Keywords
- 3D camera weight estimation, computer vision weight estimation
- Medical subject headings
- Body Weight
What is being measured
The primary outcome is the main question the study is designed to answer.
TBW estimation
Accuracy of TBW estimation using 3D camera system
Measured: Baseline
IBW estimation
Accuracy of IBW estimation using 3D camera system
Measured: Baseline
LBW estimation
Accuracy of LBW estimation using 3D camera system
Measured: Baseline
Secondary outcomes (3)
Sex-related accuracy
Difference in accuracy between males and females
Measured: Baseline
Age-related accuracy
Accuracy of weight estimation by age-group
Measured: Baseline
BMI-related accuracy
Accuracy of weight estimation by subgroup of weight status
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Oct 2024
- Study start1 Jul 2025
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Jun 2026 (estimated)
Why it stopped: No funding received.
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Body Weights and MeasuresStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Florida Atlantic University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06646120
- Sponsor's protocol ID
- 1791994(1)
- Status last verified
- Dec 2025
- First submitted
- 15 Oct 2024
- Last updated
- 19 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Cloud point data of 3D images will be shared, on request.
Publications
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
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.
PubMed 36190388Wells 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.
PubMed 36123560Wells 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.
PubMed 36277563Wells 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.
PubMed 38056057Wells 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.
PubMed 39371964
Study at a glance
From the registry record, last updated by the sponsor on 19 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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.
- ClinicalTrials.gov ID
- NCT06646120
- Acronym / sponsor ID
- 1791994(1)
- Keywords
- 3D camera weight estimation, computer vision weight estimation
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: No funding received.
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Florida Atlantic UniversityUniversity, hospital or other
- Study start
- 1 Jul 2025 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Jun 2026 (estimated)
- First posted
- 17 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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 design
- Observational model
- Other
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Students, staff and faculty at the Boca Raton campus of Florida Atlantic University.
Key inclusion criteria (1 listed)
- Any willing volunteer.
Key exclusion criteria (6 listed)
- 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;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
TBW estimation
Time frame: Baseline
Accuracy of TBW estimation using 3D camera system
IBW estimation
Time frame: Baseline
Accuracy of IBW estimation using 3D camera system
LBW estimation
Time frame: Baseline
Accuracy of LBW estimation using 3D camera system
3 secondary outcomes
Sex-related accuracy
Time frame: Baseline
Age-related accuracy
Time frame: Baseline
BMI-related accuracy
Time frame: Baseline
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Dec 2025.
- BackgroundSonar 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
- BackgroundJan 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
- Background: PubMed 36190388Wells 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.
- Background: PubMed 36123560Wells 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.
- Background: PubMed 36277563Wells 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.
- Background: PubMed 38056057Wells 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.
- Background: PubMed 39371964Wells 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.
- Individual participant dataSharing planned: Yes. Cloud point data of 3D images will be shared, on request.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Body Weights and Measures and Body Weight in the Overweight and Obese Class - I Population, run by Florida Atlantic University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 19 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
TBW estimation (time frame: Baseline). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06646120, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Dec 2025.