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Related Experiment Video

Updated: Aug 8, 2025

Creation of a High-Fidelity, Low-Cost, Intraosseous Line Placement Task Trainer via 3D Printing

Creation of a High-Fidelity, Low-Cost, Intraosseous Line Placement Task Trainer via 3D Printing

Published on: August 17, 2022

A Streamlined Algorithmic Process for Creating Three-dimensional Printed Forearm Casts.

Solaiman M Alshawaf¹, Waleed Burhamah¹, Sabika Alwazzan²

¹Division of Plastic and Reconstructive Surgery, Jaber Al Ahmad Al Sabah Hospital, Kuwait.

Plastic and Reconstructive Surgery. Global Open

|March 6, 2023

Summary

This study presents a streamlined 3D printing workflow for creating patient-specific forearm casts. The new method significantly reduces design time, enhancing efficiency in medical applications.

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Area of Science:

Medical Technology
Biomedical Engineering
Computer-Aided Design

Background:

Three-dimensional (3D) printing offers significant advancements in patient-centered medical care.
Applications include preoperative planning, custom surgical guides, implants, and patient education models.

Purpose of the Study:

To introduce a streamlined algorithmic process for designing patient-specific 3D forearm casts.
To demonstrate the efficiency of integrating 3D scanning and computer-aided design (CAD) software.

Main Methods:

Utilized an iPad with Xkelet software for forearm scanning to generate a 3D stereolithography file.
Developed an algorithmic model using Rhinoceros design software and Grasshopper plugin for cast design.
The algorithm involved mesh retopology, model division, base surface creation, clearance/thickness application, and lightweight structure design with ventilation holes.

Main Results:

The integrated scanning and algorithmic design process reduced forearm cast design time from 2-3 hours to 4-10 minutes.
This efficiency increase allows for processing a greater number of patient scans in a shorter period.

Conclusions:

The proposed method offers a quicker and more accurate approach to designing patient-specific forearm casts.
Emphasizes the value of CAD software and algorithmic modeling in optimizing medical device design workflows.