Ignite Creation Date:
2025-12-25 @ 4:09 AM
Ignite Modification Date:
2025-12-26 @ 3:07 AM
Study NCT ID:
NCT06646120
Status:
WITHDRAWN
Last Update Posted:
2025-12-19
First Post:
2024-10-15
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1
Sponsor:
Florida Atlantic University
Organization:
Study Overview
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.
Status:
WITHDRAWN
Status Verified Date:
2025-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
No funding received.
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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.
* 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 Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: