NCT05603949

Brief Summary

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.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
6

participants targeted

Target at below P25 for all trials

Timeline
Completed

Started Feb 2023

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

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Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

September 7, 2022

Completed
2 months until next milestone

First Posted

Study publicly available on registry

November 3, 2022

Completed
3 months until next milestone

Study Start

First participant enrolled

February 3, 2023

Completed
5 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

July 15, 2023

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

July 15, 2023

Completed
Last Updated

February 13, 2023

Status Verified

February 1, 2023

Enrollment Period

5 months

First QC Date

September 7, 2022

Last Update Submit

February 10, 2023

Conditions

Skull Defect

Keywords

Deep Neural Networks;3D Shape Inpainting

Outcome Measures

Primary Outcomes (2)

  • Number of patients where there is no need to adapt the Patient Specific Implant (PSI) edges

    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

    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

Study Arms (1)

experimental group

Device: 3D deep learning neural network system

Interventions

3D deep learning neural network systemDEVICE

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.

experimental group

Eligibility Criteria

Age15 Years - 80 Years
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Take a medical center as the research object

You may qualify if:

  • Scheduled for cranioplasty
  • Informed consent

You may not qualify if:

  • (1)No informed consent

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Linkou Chang Gung Memorial Hospital

Taoyuan, 333, Taiwan

RECRUITING

Central Study Contacts

Yau-zen chang

CONTACT

(03)211-8800zen@mail.cgu.edu.tw

Study Design

Study Type
observational
Observational Model
CASE ONLY
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Professor

Study Record Dates

First Submitted

September 7, 2022

First Posted

November 3, 2022

Study Start

February 3, 2023

Primary Completion

July 15, 2023

Study Completion

July 15, 2023

Last Updated

February 13, 2023

Record last verified: 2023-02

Locations

TW(1)