NCT05603949

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 6 (estimated)
• Sponsor: Chang Gung Memorial Hospital · Academic / Other
• Sex: All
• Age: 15 Years – 80 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Skull Defect

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2023-02-03
• Primary completion: 2023-07-15
• Completion: 2023-07-15
• First posted: 2022-11-03
• Last updated: 2023-02-13

Locations

1 site across 1 country: Taiwan