Skull DefectNCT05603949
Development of Three-dimensional Deep Learning for Automatic Design of Skull Implants
An observational study of Skull Defect, run by Chang Gung Memorial Hospital at 1 site in Taiwan.
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. Read more about this study
- Who is it for?
- Ages 15 to 80 years
- Conditions the study is for
- Skull Defect
- What is being tested?
- 3D deep learning neural network system
- Type of treatment
- Medical device
- How many people
- 6 participants
- Where
- 1 siteTaiwan
- Study dates
- 3 Feb 2023to 15 Jul 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 15 to 80 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Take a medical center as the research object
Who may be able to join
- Scheduled for cranioplasty
- Informed consent
Who cannot join
- (1)No informed consent
Trial locations and contacts
1 site in 1 country.
Taiwan1 site, 1 recruiting
- Recruiting
Linkou Chang Gung Memorial Hospital
Taoyuan City, 333
Yau-zen Chang, PhD
About this study
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.
Read the 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
- Being tested
- 3D deep learning neural network system
- Keywords
- Deep Neural Networks;3D Shape Inpainting
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| experimental group | Not described Receives: Device: 3D deep learning neural network system |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 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
Measured: 6 weeks after surgery by standardised questionnaire
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Nov 2022
- Study start3 Feb 2023
- Primary completion15 Jul 2023 (estimated)
- Study completion15 Jul 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Skull DefectStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 6 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chang Gung Memorial Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Ministry of Science and Technology, Taiwan
- Responsible party
- Principal investigator, Yau-Zen Chang, Chang Gung Memorial Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05603949
- Sponsor's protocol ID
- 202201082B0
- Status last verified
- Feb 2023
- First submitted
- 7 Sep 2022
- Last updated
- 13 Feb 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of Three-dimensional Deep Learning for Automatic Design of Skull Implants
- ClinicalTrials.gov ID
- NCT05603949
- Acronym / sponsor ID
- 202201082B0
- Conditions
- Skull Defect
- Keywords
- Deep Neural Networks;3D Shape Inpainting
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 6 participants (estimated)
- Lead sponsor
- Chang Gung Memorial HospitalUniversity, hospital or other
- Collaborators
- Ministry of Science and Technology, Taiwan
- Study start
- 3 Feb 2023 (actual)
- Primary completion
- 15 Jul 2023 (estimated)
- Study completion
- 15 Jul 2023 (estimated)
- First posted
- 3 Nov 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| experimental group | – | Device: 3D deep learning neural network system |
Who may qualify
- Ages
- 15 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Take a medical center as the research object
Key inclusion criteria (2 listed)
- Scheduled for cranioplasty
- Informed consent
Key exclusion criteria (1 listed)
- (1)No informed consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 3D deep learning neural network systemWith 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. | Device | experimental group |
Primary outcome
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
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
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Feb 2023.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Skull Defect, run by Chang Gung Memorial Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 13 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yau-zen chang ((03)211-8800 ext. 5341, zen@mail.cgu.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Feb 2023.