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

Skull Defect Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05603949
• Other study ID #: 202201082B0
• Status: Recruiting
• Start date: February 3, 2023
• Est. completion date: July 15, 2023

Study information

• Verified date: February 2023
• Source: Chang Gung Memorial Hospital
• Contact: Yau-zen chang
• Phone: (03)211-8800
• Email: zen[@]mail.cgu.edu.tw
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 6
• Est. completion date: July 15, 2023
• Est. primary completion date: July 15, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 15 Years to 80 Years

Eligibility

Inclusion Criteria:

1. Scheduled for cranioplasty

2. Informed consent

Exclusion Criteria:

(1)No informed consent

Related Conditions & MeSH terms

• Skull Defect

Intervention

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.

Locations

Country	Name	City	State
Taiwan	Linkou Chang Gung Memorial Hospital	Taoyuan City

Sponsors (2)

Lead Sponsor	Collaborator
Chang Gung Memorial Hospital	Ministry of Science and Technology, Taiwan

Country where clinical trial is conducted

Taiwan, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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