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Related Concept Videos

Apparent Weight01:09

Apparent Weight

True weight is the measure of the gravitational force acting on an object. However, if the object accelerates, its measured weight is different from its true weight. Similar observations can be made when the object is submerged in water. An object's weight in water is its apparent weight, which is equal to the difference between its true weight and the buoyant forces.
Consider a person standing on a bathroom scale inside an elevator. If the scale is accurate at rest, its reading equals the...

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A Novel Artificial Intelligence-Based Mobile Application for Pediatric Weight Estimation.

Sungwoo Choi1, Sangun Nah1, Ji Eun Moon2

  • 1Department of Emergency Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea.

Journal of Clinical Medicine
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

A new AI camera app (PAICam) estimates pediatric weight using pose estimation, showing accuracy comparable to the Broselow tape in emergencies. This tool offers a potential alternative for rapid, reliable weight assessment in critical situations.

Keywords:
artificial intelligencebody weightdeep learningpediatrics

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Area of Science:

  • Medical Technology
  • Artificial Intelligence in Healthcare
  • Pediatric Emergency Medicine

Background:

  • Accurate pediatric weight estimation is crucial for emergency drug dosing.
  • Traditional length-based tools may be inaccurate in restricted positions.
  • Novel AI-driven methods are needed for improved accuracy and accessibility.

Purpose of the Study:

  • To develop and evaluate a weight prediction application (PAICam) using human pose estimation and deep neural networks.
  • To compare the accuracy of PAICam against the Broselow tape for pediatric weight estimation.
  • To assess the clinical utility of PAICam in emergency scenarios.

Main Methods:

  • Prospective cross-sectional study (June 2023-May 2024) including 1335 pediatric patients (1 month-12 years).
  • Weight estimation accuracy assessed using Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Percentage Error (RMSPE).
  • Comparison of PAICam and Broselow tape (BT) performance, including percentages of weight estimations within 10% (PW10) and 20% (PW20).

Main Results:

  • PAICam and BT demonstrated comparable performance across all error metrics (MPE, MAPE, RMSPE).
  • Similar percentages of accurate weight estimations were observed: PW10 (52.6% BT vs. 51.2% PAICam) and PW20 (79.1% BT vs. 77.7% PAICam).
  • Strong agreement (ICC values 0.959 vs. 0.955) was found between actual and predicted weights for both methods.

Conclusions:

  • PAICam, leveraging deep learning and pose estimation, is a viable alternative for pediatric weight estimation.
  • The AI-powered tool offers accuracy comparable to the established Broselow tape.
  • PAICam shows potential for improving pediatric weight assessment in emergency settings.