Artificial Intelligence Based Autonomous Socket Proposal Program: Socket Design Experiences

Study Overview
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Brief Summary
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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
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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.

Official Title
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Artificial Intelligence Based Autonomous Socket Proposal Program: Socket Design Experiences

Conditions
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Artificial Intelligence, Prosthetic

Intervention / Treatment
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* Other: the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: - Conscious patients >18 years old having undergone amputation surgery Exclusion Criteria: • 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.)

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Model of the stump scanned with a 3d scanner<br>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 | Other: the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.<br>* the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.<br>|
| Socket matched to stump<br>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 the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 2 years |