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Predicción de componentes protésicos en artroplastia de cadera empleando aprendizaje multimodal

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Puche Ajiaco, Mario Andrés
Abstract (Spanish)
El presente estudio propone un algoritmo basado en una red neuronal multimodal para predecir uno de los componentes protésicos: el diámetro acetabular. Este algoritmo emplea imágenes diagnósticas, como radiografías anteroposteriores de cadera, junto con la información del género del paciente. La implementación consta de dos etapas. En la primera, se utiliza una arquitectura U-Net empleada para segmentar las radiografías. En la segunda etapa, una red neuronal predice el diámetro acetabular a partir de las imágenes diagnósticas y los datos tabulares.
Abstract (English)
The present study proposes an algorithm based on a multimodal neural network to predict one of the prosthetic components: the acetabular diameter. This algorithm uses diagnostic images, such as anteroposterior hip radiographs, along with the patient’s gender information. The implementation consists of two stages. In the first, a U-Net architecture is used to segment the radiographs. In the second stage, a neural network predicts the acetabular diameter from the diagnostic images and tabular data.
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66 páginas
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URI: https://repositorio.escuelaing.edu.co/handle/001/3632
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