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Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Related Experiment Video

Updated: Jul 5, 2025

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
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Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development

Abderrahmen Masmoudi1, Amine Zouari1, Ahmed Bouzid1

  • 1Surgery Department, Habib Bourguiba University Hospital, Sfax, Tunisia.

Jmirx Med
|January 18, 2024
PubMed
Summary
This summary is machine-generated.

A new mobile app, Measure It, accurately estimates waist circumference (WC) from CT scans, providing a valuable tool for assessing obesity. This mHealth solution facilitates routine WC measurement in clinical and research settings.

Keywords:
BMICTCT imageCT scanabdominalabdominal CTappapplicationbodybody massclinicalcomputed tomographydesignhealth appsmedicalmobile appmobile healthmorbiditymortalityobesityprototypetoolusabilityvaliditywaistwaist circumference

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Area of Science:

  • Medical Imaging
  • Mobile Health (mHealth)
  • Obesity Research

Background:

  • Waist circumference (WC) is a key indicator of morbidity and mortality, offering insights beyond BMI.
  • Routine clinical measurement of WC is not standard practice.
  • Mobile health (mHealth) offers potential for accessible WC measurement from computed tomography (CT) scans.

Purpose of the Study:

  • To develop a mobile application for measuring waist circumference (WC) using cross-sectional CT images.
  • To create a user-friendly mHealth tool for abdominal obesity assessment.

Main Methods:

  • Developed the 'Measure It' mobile app prototype for WC estimation from CT scans.
  • Validated the app by comparing its measurements to conventional tape measurements in 20 patients.
  • Employed statistical analyses including Pearson correlation, t-tests, and Bland-Altman plots.
  • Evaluated the app's accuracy in detecting abdominal obesity.

Main Results:

  • The 'Measure It' app demonstrated a strong correlation (Pearson R=0.906) with traditional WC measurements.
  • No significant differences were found between app-based and conventional WC measurements (P=.98).
  • The app achieved 83% accuracy in identifying abdominal obesity.

Conclusions:

  • The 'Measure It' app is a practical mHealth tool for routine waist circumference measurement.
  • This tool can enhance the assessment of obesity in clinical and research environments.
  • Further validation and usability studies are planned before widespread clinical adoption.