Evidence map›Paper›PMID 39371964›Full record

ArticleJournal of the American College of Emergency Physicians open2024

Total body weight estimation by 3D camera systems: Potential high-tech solutions for emergency medicine applications? A scoping review.

Mike Wells, Lara Nicole Goldstein, Terran Wells, Niloufar Ghazi, Abhijit Pandya, Borifoje Furht, Gabriella Engstrom, Muhammad Tanveer Jan, Richard Shih

3 registry-linked trialsAbstract readScoping Review
In one paragraph

Article in Journal of the American College of Emergency Physicians open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06645548 nawithdrawnnot on this mapstarted 2029, after this paper: background citation

Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 3 - Measure the Accuracy of Weight Estimations by The 3D Camera System in Acutely Ill or Injured Emergency Department Patients and Compare This Accuracy Against That of Standard Care.

TypeinterventionalSponsorFlorida Atlantic UniversityRan2029 to 2030Enrolled0ConditionsBody Weights and Measures, Body Weight in the Overweight and Obese Class - I Population, Drug Dose, Weight EstimateArmsWeight estimation using 3D camera
NCT06646120 withdrawnnot on this mapstarted 2025, after this paper: background citation

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.

TypeobservationalSponsorFlorida Atlantic UniversityRan2025 to 2026Enrolled0ConditionsBody Weights and Measures, Body Weight in the Overweight and Obese Class - I Population
NCT06646133 withdrawnnot on this mapstarted 2027, after this paper: background citation

Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care: Study 2 - Measure the Accuracy and Speed of Weight Estimations Using a 3D Camera System During Simulated Medical Emergencies

TypeobservationalSponsorFlorida Atlantic UniversityRan2027 to 2028Enrolled0ConditionsBody Weights and Measures, Body Weight in the Overweight and Obese Class - I Population, Weight Estimation, Emergency Drug DosingArms3D camera weight estimate, Standard methods of weight estimation
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mike WellsDepartment of Emergency Medicine Schmidt College of Medicine Florida Atlantic University Boca Raton Florida USA.ORCID https://orcid.org/0000-0002-4520-2007
Lara Nicole GoldsteinDepartment of Emergency Medicine Schmidt College of Medicine Florida Atlantic University Boca Raton Florida USA.
Terran WellsDepartment of Biomedical Engineering Florida International University Miami Florida USA.
Niloufar GhaziDepartment of Emergency Medicine Schmidt College of Medicine Florida Atlantic University Boca Raton Florida USA.
Abhijit PandyaDepartment of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton Florida USA.
Borifoje FurhtDepartment of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton Florida USA.
Gabriella EngstromDepartment of Emergency Medicine Schmidt College of Medicine Florida Atlantic University Boca Raton Florida USA.
Muhammad Tanveer JanDepartment of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton Florida USA.
Richard ShihDepartment of Emergency Medicine Schmidt College of Medicine Florida Atlantic University Boca Raton Florida USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Weight estimation is required in adult patients when weight-based medication must be administered during emergency care, as measuring weight is often not possible. Inaccurate estimations may lead to inaccurate drug dosing, which may cause patient harm. High-tech 3D camera systems driven by artificial intelligence might be the solution to this problem. The aim of this review was to describe and evaluate the published literature on 3D camera weight estimation methods. Methods: A systematic literature search was performed for articles that studied the use of 3D camera systems for weight estimation in adults. Data on the study characteristics, the quality of the studies, the 3D camera methods evaluated, and the accuracy of the systems were extracted and evaluated. Results: A total of 14 studies were included, published from 2012 to 2024. Most studies used Microsoft Kinect cameras, with various analytical approaches to weight estimation. The 3D camera systems often achieved a P10 of 90% (90% of estimates within 10% of actual weight), with all systems exceeding a P10 of 78%. The studies highlighted a significant potential for 3D camera systems to be suitable for use in emergency care. Conclusion: The 3D camera systems offer a promising method for weight estimation in emergency settings, potentially improving drug dosing accuracy and patient safety. Weight estimates were satisfactorily accurate, often exceeding the reported accuracy of existing weight estimation methods. Importantly, 3D camera systems possess characteristics that could make them very appropriate for use during emergency care. Future research should focus on developing and validating this methodology in larger studies with true external and clinical validation.

Indexed as

3D camera weight estimationdrug dosingweight estimation

Identifiers

PMID39371964
PMCPMC11452255

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.