Enhancing Medical Diagnosis and Treatment Planning through Automated Acquisition and Classification of Bone Fracture Patterns
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The extraction of the main features of a fractured bone area enables subsequent virtual reproduction for bone simulations. Exploring the fracture zone for other applications remains largely unexplored in current research. Recreating and analyzing fracture patterns has direct applications in medical training programs for traumatologists, automatic bone fracture reduction algorithms, and diagnostics. Furthermore, pattern classification aids in establishing treatment guidelines that specialists can follow during the surgical process. This paper focuses on the process of obtaining an accurate representation of bone fractures, starting with computed tomography scans, and subsequently classifying these patterns using a convolutional neural network. The proposed methodology aims to streamline the extraction and classification of fractures from clinical cases, contributing to enhanced diagnosis and medical simulation applications.
Description
CCS Concepts: Computing methodologies → Artificial intelligence; Machine learning; Computer graphics
@inproceedings{10.2312:ceig.20241140,
booktitle = {Spanish Computer Graphics Conference (CEIG)},
editor = {Marco, Julio and Patow, Gustavo},
title = {{Enhancing Medical Diagnosis and Treatment Planning through Automated Acquisition and Classification of Bone Fracture Patterns}},
author = {Pérez-Cano, Francisco Daniel and Parra-Cabrera, Gema and Camacho-García, Rubén and Jiménez, Juan José},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-261-5},
DOI = {10.2312/ceig.20241140}
}