Artificial IntelligenceNCT05341674
Artificial Intelligence Based Autonomous Socket Proposal Program: Socket Design Experiences
An observational study of Artificial Intelligence and Prosthetic, run by Hasan Kalyoncu University at 1 site in Turkey (Türkiye).
Trial summary
The aim of this study is to develop an artificial intelligence-based autonomous socket recommendation program that will provide a more comfortable and easier test socket production with high time-cost efficiency and to share experiences about socket designs in these processes. Read more about this study
- Who is it for?
- Ages 18 to 65 years
- Conditions the study is for
- Artificial Intelligence, Prosthetic
- What is being tested?
- the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.
- How many people
- 101 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Jan 2020to 1 Mar 2022
- 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 18 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients aged between 18-45 years with amputation who came to Hasan Kalyoncu University and who met the inclusion criteria of the study.
Who may be able to join
- Conscious patients >18 years old having undergone amputation surgery
Who cannot join
- • Severe visual and perception impairment
- Surgical intervention with functional sequelae in the extremities
- Pain that does not allow tests to be done
- Patients with diseases with neurological dysfunction (stroke, multiple sclerosis, etc.)
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Hasan Kalyoncu University
Gaziantep, Şahinbey, 27000
About this study
The aim of this study is to develop an artificial intelligence-based autonomous socket recommendation program that will provide a more comfortable and easier test socket production with high time-cost efficiency and to share experiences about socket designs in these processes.
Read the detailed description
For the artificial intelligence-based software planned to be created, the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner. The scanned patterns were saved as point clouds. The socket parts of the prostheses used by the same patients were also scanned with the same scanner device and recorded.
The point dataset consisting of stump-socket matches obtained from the patients was used for the software.
In order to train the artificial intelligence model, a working environment has been created in which artificial intelligence libraries and tools can be used on the computer. For this purpose, first Anaconda data science platform was established. Thereupon, Python programming language and Tensorflow deep learning library were installed, other libraries required for the training of the artificial intelligence model were added, and the working environment was made ready. A deep learning algorithm was used in the artificial intelligence model developed for training the data. The purpose of using deep learning, which is one of the most up-to-date and popular artificial intelligence algorithms, is to achieve more accurate results by increasing the performance and accuracy rate. First, the dataset is 90% reserved for training and 10% for testing. Then, a deep learning model was created with the Sequantial() model selected from the Keras library. In the model, a total of 7 layers are used, the first of which is the input layer and the last is the output layer. While "relu" is used as the activation function for the input layer and intermediate layers, the "linear" function is used for the output layer. While creating the model, "Adam" was chosen as the optimizer. In the model trained with a total of 500 "repetitions", "batch size" is assigned as 5. The trained model was then tested with the test data and a success rate of 61% was achieved. Afterwards, the model and weights were recorded. After the model training was completed, a new Python program was developed. The previously developed models and weights were loaded while the program was running and were used to propose a socket for the new die data to be given. When the program is run, the stump name for which a socket is requested is asked.
Thus, the program proposes a new socket after receiving the stubby data set from the user and testing it in the trained model. This 3D socket model is shown to the user via the Python Plotly Graphics Library.
- Conditions
- Artificial Intelligence, Prosthetic
- Being tested
- the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.Other
the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.
| Study group | Role | What they receive |
|---|---|---|
| Model of the stump scanned with a 3d scanner | For the artificial intelligence-based software planned to be created, the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner. The scanned patterns were saved as point clouds Receives: Other: the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner. | |
| Socket matched to stump | The socket parts of the prostheses used by the same patients (with other group) were also scanned with the same scanner device and recorded. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Software ( Artificial Intelligence Based Autonomous Socket Proposal Program)
The foresight of the software to be developed will be evaluated. It will be evaluated how suitable a socket design can be suggested for the stump dimensions entered into the system.
Thanks to the software, the time taken for socket design will be compared with the time taken for sockets produced with classical methods.
The time/cost effectiveness of the software will be evaluated.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Apr 2022
- Study start1 Jan 2020
- Primary completion10 Jun 2021
- Study completion1 Mar 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Artificial IntelligenceStudies 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-control
- Time perspective
- Retrospective
- Enrolment
- 101 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hasan Kalyoncu University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Murat Ali ÇINAR, Hasan Kalyoncu University
- Investigators
- Murat ÇINAR, Doctor, Principal investigator, Hasan Kalyoncu University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05341674
- Sponsor's protocol ID
- MAC2022
- Status last verified
- Apr 2022
- First submitted
- 10 Apr 2022
- Last updated
- 22 Apr 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Ten Kate J, Smit G, Breedveld P. 3D-printed upper limb prostheses: a review. Disabil Rehabil Assist Technol. 2017 Apr;12(3):300-314. doi: 10.1080/17483107.2016.1253117. Epub 2017 Feb 2.
PubMed 28152642O'Brien L, Cho E, Khara A, Lavranos J, Lommerse L, Chen C. 3D-printed custom-designed prostheses for partial hand amputation: Mechanical challenges still exist. J Hand Ther. 2021 Oct-Dec;34(4):539-542. doi: 10.1016/j.jht.2020.04.005. Epub 2020 Jun 19.
PubMed 32565103Vujaklija I, Farina D. 3D printed upper limb prosthetics. Expert Rev Med Devices. 2018 Jul;15(7):505-512. doi: 10.1080/17434440.2018.1494568. Epub 2018 Jul 5.
PubMed 29949397Abbady HEMA, Klinkenberg ETM, de Moel L, Nicolai N, van der Stelt M, Verhulst AC, Maal TJJ, Brouwers L. 3D-printed prostheses in developing countries: A systematic review. Prosthet Orthot Int. 2022 Feb 1;46(1):19-30. doi: 10.1097/PXR.0000000000000057.
PubMed 34772868
Study at a glance
From the registry record, last updated by the sponsor on 22 Apr 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Based Autonomous Socket Proposal Program: Socket Design Experiences
- ClinicalTrials.gov ID
- NCT05341674
- Acronym / sponsor ID
- MAC2022
- Conditions
- Artificial Intelligence, Prosthetic
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 101 participants (actual)
- Lead sponsor
- Hasan Kalyoncu UniversityUniversity, hospital or other
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 10 Jun 2021 (actual)
- Study completion
- 1 Mar 2022 (actual)
- First posted
- 22 Apr 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.
The aim of this study is to develop an artificial intelligence-based autonomous socket recommendation program that will provide a more comfortable and easier test socket production with high time-cost efficiency and to share experiences about socket designs in these processes.
Detailed description
For the artificial intelligence-based software planned to be created, the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner. The scanned patterns were saved as point clouds. The socket parts of the prostheses used by the same patients were also scanned with the same scanner device and recorded.
The point dataset consisting of stump-socket matches obtained from the patients was used for the software.
In order to train the artificial intelligence model, a working environment has been created in which artificial intelligence libraries and tools can be used on the computer. For this purpose, first Anaconda data science platform was established. Thereupon, Python programming language and Tensorflow deep learning library were installed, other libraries required for the training of the artificial intelligence model were added, and the working environment was made ready. A deep learning algorithm was used in the artificial intelligence model developed for training the data. The purpose of using deep learning, which is one of the most up-to-date and popular artificial intelligence algorithms, is to achieve more accurate results by increasing the performance and accuracy rate. First, the dataset is 90% reserved for training and 10% for testing. Then, a deep learning model was created with the Sequantial() model selected from the Keras library. In the model, a total of 7 layers are used, the first of which is the input layer and the last is the output layer. While "relu" is used as the activation function for the input layer and intermediate layers, the "linear" function is used for the output layer. While creating the model, "Adam" was chosen as the optimizer. In the model trained with a total of 500 "repetitions", "batch size" is assigned as 5. The trained model was then tested with the test data and a success rate of 61% was achieved. Afterwards, the model and weights were recorded. After the model training was completed, a new Python program was developed. The previously developed models and weights were loaded while the program was running and were used to propose a socket for the new die data to be given. When the program is run, the stump name for which a socket is requested is asked.
Thus, the program proposes a new socket after receiving the stubby data set from the user and testing it in the trained model. This 3D socket model is shown to the user via the Python Plotly Graphics Library.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Model of the stump scanned with a 3d scanner | – | Other: the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.For the artificial intelligence-based software planned to be created, the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner. The scanned patterns were saved as point clouds |
| Socket matched to stump | – | –The socket parts of the prostheses used by the same patients (with other group) were also scanned with the same scanner device and recorded. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients aged between 18-45 years with amputation who came to Hasan Kalyoncu University and who met the inclusion criteria of the study.
Key inclusion criteria (1 listed)
- Conscious patients >18 years old having undergone amputation surgery
Key exclusion criteria (4 listed)
- • Severe visual and perception impairment
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 |
|---|---|---|
| the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner. | Other | Model of the stump scanned with a 3d scanner |
Primary outcome
Software ( Artificial Intelligence Based Autonomous Socket Proposal Program)
Time frame: 2 years
The foresight of the software to be developed will be evaluated. It will be evaluated how suitable a socket design can be suggested for the stump dimensions entered into the system. Thanks to the software, the time taken for socket design will be compared with the time taken for sockets produced with classical methods. The time/cost effectiveness of the software will be evaluated.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Apr 2022.
- Results publication: PubMed 28152642Ten Kate J, Smit G, Breedveld P. 3D-printed upper limb prostheses: a review. Disabil Rehabil Assist Technol. 2017 Apr;12(3):300-314. doi: 10.1080/17483107.2016.1253117. Epub 2017 Feb 2.
- Results publication: PubMed 32565103O'Brien L, Cho E, Khara A, Lavranos J, Lommerse L, Chen C. 3D-printed custom-designed prostheses for partial hand amputation: Mechanical challenges still exist. J Hand Ther. 2021 Oct-Dec;34(4):539-542. doi: 10.1016/j.jht.2020.04.005. Epub 2020 Jun 19.
- Results publication: PubMed 29949397Vujaklija I, Farina D. 3D printed upper limb prosthetics. Expert Rev Med Devices. 2018 Jul;15(7):505-512. doi: 10.1080/17434440.2018.1494568. Epub 2018 Jul 5.
- Results publication: PubMed 34772868Abbady HEMA, Klinkenberg ETM, de Moel L, Nicolai N, van der Stelt M, Verhulst AC, Maal TJJ, Brouwers L. 3D-printed prostheses in developing countries: A systematic review. Prosthet Orthot Int. 2022 Feb 1;46(1):19-30. doi: 10.1097/PXR.0000000000000057.
- Individual participant dataSharing planned: No
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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.
Murat ÇINAR, Doctor principal investigator
Hasan Kalyoncu University
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 Artificial Intelligence and Prosthetic, run by Hasan Kalyoncu University at 1 site in Turkey (Türkiye).
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 1 inclusion and 4 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: finished, as of the sponsor's last update on 22 Apr 2022. 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 Turkey (Türkiye). 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Software ( Artificial Intelligence Based Autonomous Socket Proposal Program) (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05341674, which also lists 4 linked publications.
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 22 Apr 2022.