Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Exploring three-dimensional reconstruction with Neural Radiance Field (NeRF) for coronary roadmap navigation and view-planning in X-ray coronary angiography: A feasibility study.

Computer methods and programs in biomedicine·2026

Same author

Subclavian or axillary artery cannulation for extracorporeal membrane oxygenation: A systematic review.

JTCVS open·2026

Same author

Verification of a Fluid-Structure Interaction Model for Aortic Stenosis Through Comparison With In Vitro Experiments.

International journal for numerical methods in biomedical engineering·2026

Same author

Patient-specific lung simulation incorporating regional elastance and recruitment to guide mechanical ventilation in acute respiratory distress syndrome.

Journal of applied physiology (Bethesda, Md. : 1985)·2026

Same author

Extracorporeal Membrane Oxygenation Related Axillary Artery Cannulation and Aortic Hemodynamics: Insights From Computational Fluid Dynamics.

ASAIO journal (American Society for Artificial Internal Organs : 1992)·2026

Same author

The hemodynamic impact of congenital heart diseases during fetal-to-neonatal transition: an in-silico investigation.

Pediatric research·2026

Search research articles

Related Experiment Video

Updated: Jul 30, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Deep Learning Method for Estimation of Morphological Parameters Based on CT Scans.

Rajarajeswari Ganesan¹, Antonino A La Mattina^2,3, Frans N van de Vosse¹

¹Eindhoven University of Technology, The Netherlands.

Studies in Health Technology and Informatics

|May 19, 2023

Summary

This study introduces a Convolutional Neural Network (CNN) for estimating body height and weight. The method accurately predicts these parameters even with limited training data.

Keywords:

CNN Deep Learning Electronic Health Records (EHRs)Height Weight

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Related Experiment Videos

Last Updated: Jul 30, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Area of Science:

Biomedical Engineering
Machine Learning
Medical Imaging

Background:

Accurate estimation of body height and weight is crucial for clinical assessments and health monitoring.
Traditional methods may require direct measurements or extensive datasets, posing challenges in certain scenarios.

Purpose of the Study:

To develop and evaluate a novel Convolutional Neural Network (CNN) model for estimating body height and weight.
To assess the efficacy of the proposed CNN model when trained on limited datasets.

Main Methods:

A Convolutional Neural Network (CNN) architecture was designed, incorporating an assembly of non-linear fully connected layers.
The CNN model was trained and validated using a limited dataset for the task of predicting body height and weight.

Main Results:

The proposed CNN model demonstrated the ability to estimate body height and weight within acceptable clinical limits.
Effective prediction was achieved even when the model was trained using a restricted amount of data.

Conclusions:

The developed CNN-based approach offers a viable solution for estimating body height and weight, particularly in data-scarce environments.
This method shows potential for clinical applications where efficient and accurate anthropometric estimations are required.