Artificial Intelligence Based Autonomous Socket Proposal Program: Socket Design Experiences
Condition: Artificial Intelligence; Prosthetic
Study type: Observational

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.
    

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.
    

Intervention name(s): the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.
Intervention type(s): Other
Intervention description(s): the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.
Observational model: Case-Control
Time perspective: Retrospective
Sampling method: Non-Probability Sample
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.