Development of Three-dimensional Deep Learning for Automatic Design of Skull Implants

February 10, 2023 updated by: Yau-Zen Chang, Chang Gung Memorial Hospital
This project aims to develop an effective deep learning system to generate numerical implant geometry based on 3D defective skull models from CT scans. This technique is beneficial for the design of implants to repair skull defects above the Frankfort horizontal plane.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Designing a personalized implant to restore the protective and aesthetic functions of the patient's skull is challenging. The skull defects may be caused by trauma, congenital malformation, infection, and iatrogenic treatments such as decompressive craniectomy, plastic surgery, and tumor resection. The project aims to develop a deep learning system with 3D shape reconstruction capabilities. The system will meet the requirement of designing high-resolution 3D implant numerical models efficiently.

A collection of skull images were used for training the deep learning system. Defective models in the datasets were created by numerically masking areas of intact 3D skull models. The final implant design should be verified by neurosurgeons using 3D printed models.

Study Type

Observational

Enrollment (Anticipated)

6

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Taiwan
      • Taoyuan City, Taiwan, 333
        • Recruiting
        • Linkou Chang Gung Memorial Hospital
        • Contact:
          • Yau-zen Chang, PhD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

15 years to 80 years (ADULT, OLDER_ADULT, CHILD)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Take a medical center as the research object

Description

Inclusion Criteria:

  1. Scheduled for cranioplasty
  2. Informed consent

Exclusion Criteria:

(1)No informed consent

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Case-Only
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
experimental group
Device: 3D deep learning neural network system
With the consent of the patient, we will assist in the production of images of 3D defect blocks for free (3D deep learning neural network system (3D DNN) system process planning), complete the repair and reconstruction under the clinical routine surgery, and track the repair results after surgery. meet medical needs.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Number of patients where there is no need to adapt the Patient Specific Implant (PSI) edges
Time Frame: 6 weeks after surgery by standardised questionnaire
Number of patients where there is no need to adapt the Patient Specific Implant (PSI) edges
6 weeks after surgery by standardised questionnaire
Number of patients where there is no need to augment/fill clefts between the Patient Specific Implant (PSI) and patient´s bone
Time Frame: 6 weeks after surgery by standardised questionnaire
Number of patients where there is no need to augment/fill clefts between the Patient Specific Implant (PSI) and patient´s bone
6 weeks after surgery by standardised questionnaire

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chang Gung Memorial Hospital

Collaborators

Ministry of Science and Technology, Taiwan

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (ACTUAL)

February 3, 2023

Primary Completion (ANTICIPATED)

July 15, 2023

Study Completion (ANTICIPATED)

July 15, 2023

Study Registration Dates

First Submitted

September 7, 2022

First Submitted That Met QC Criteria

November 1, 2022

First Posted (ACTUAL)

November 3, 2022

Study Record Updates

Last Update Posted (ACTUAL)

February 13, 2023

Last Update Submitted That Met QC Criteria

February 10, 2023

Last Verified

February 1, 2023

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 202201082B0

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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