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

Study Overview
=================
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.

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.

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

Conditions
-----------------
Skull Defect

Intervention / Treatment
-----------------
* Device: 3D deep learning neural network system


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: Scheduled for cranioplasty Informed consent Exclusion Criteria: (1)No informed consent

Ages Eligible for Study
-----------------
Minimum Age: 15 Years
Maximum Age: 80 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| experimental group<br> | Device: 3D deep learning neural network system<br>* 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.<br>|

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 | 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 | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Deep Neural Networks;3D Shape Inpainting