Clinical trial NCT06646120: Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1
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.
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Body Weights and Measures
Body Weight in the Overweight and Obese Class - I Population
Keywords: 3D camera weight estimation
computer vision weight estimation
Lead sponsor: Florida Atlantic University
Sponsor class: OTHER
First posted: 2024-10-17
Enrollment: 800
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Students, staff and faculty at the Boca Raton campus of Florida Atlantic University.

Brief 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. 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.

Eligibility criteria: 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.

Primary outcomes:
measure: TBW estimation
timeFrame: Baseline
measure: IBW estimation
timeFrame: Baseline
measure: LBW estimation
timeFrame: Baseline
Secondary outcomes:
measure: Sex-related accuracy
timeFrame: Baseline
measure: Age-related accuracy
timeFrame: Baseline
measure: BMI-related accuracy
timeFrame: Baseline
Other outcomes:
Locations:
Study officials:
References:
citation: 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
citation: 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
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.