Enhancing Medical Diagnosis and Treatment Planning through Automated Acquisition and Classification of Bone Fracture Patterns

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dc.contributor.authorPérez-Cano, Francisco Danielen_US
dc.contributor.authorParra-Cabrera, Gemaen_US
dc.contributor.authorCamacho-García, Rubénen_US
dc.contributor.authorJiménez, Juan Joséen_US
dc.contributor.editorMarco, Julioen_US
dc.contributor.editorPatow, Gustavoen_US
dc.date.accessioned2024-06-03T14:51:27Z
dc.date.available2024-06-03T14:51:27Z
dc.date.issued2024
dc.description.abstractThe 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.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationSpanish Computer Graphics Conference (CEIG)
dc.identifier.doi10.2312/ceig.20241140
dc.identifier.isbn978-3-03868-261-5
dc.identifier.pages8 pages
dc.identifier.urihttps://doi.org/10.2312/ceig.20241140
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/ceig20241140
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Artificial intelligence; Machine learning; Computer graphics
dc.subjectCCS Concepts
dc.subjectComputing methodologies → Artificial intelligence
dc.subjectMachine learning
dc.subjectComputer graphics
dc.titleEnhancing Medical Diagnosis and Treatment Planning through Automated Acquisition and Classification of Bone Fracture Patternsen_US
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