Pérez-Cano Francisco Daniel, Parra-Cabrera Gema, Vilchis-Torres Ivett, Reyes-Lagos José Javier, Jiménez-Delgado Juan José
Department of Computer Science, University of Jaén, 23071 Jaén, Spain.
Centro de Investigación Multidisciplinaria en Educación, Universidad Autónoma del Estado de México, Toluca 50110, Mexico.
J Pers Med. 2024 Mar 30;14(4):376. doi: 10.3390/jpm14040376.
Fracture pattern acquisition and representation in human bones play a crucial role in medical simulation, diagnostics, and treatment planning. This article presents a comprehensive review of methodologies employed in acquiring and representing bone fracture patterns. Several techniques, including segmentation algorithms, curvature analysis, and deep learning-based approaches, are reviewed to determine their effectiveness in accurately identifying fracture zones. Additionally, diverse methods for representing fracture patterns are evaluated. The challenges inherent in detecting accurate fracture zones from medical images, the complexities arising from multifragmentary fractures, and the need to automate fracture reduction processes are elucidated. A detailed analysis of the suitability of each representation method for specific medical applications, such as simulation systems, surgical interventions, and educational purposes, is provided. The study explores insights from a broad spectrum of research articles, encompassing diverse methodologies and perspectives. This review elucidates potential directions for future research and contributes to advancements in comprehending the acquisition and representation of fracture patterns in human bone.
人体骨骼骨折模式的获取与呈现在医学模拟、诊断及治疗规划中发挥着关键作用。本文全面综述了用于获取和呈现骨骼骨折模式的方法。对包括分割算法、曲率分析和基于深度学习的方法在内的多种技术进行了综述，以确定它们在准确识别骨折区域方面的有效性。此外，还评估了表示骨折模式的各种方法。阐明了从医学图像中检测准确骨折区域所固有的挑战、多片段骨折产生的复杂性以及骨折复位过程自动化的需求。详细分析了每种表示方法对于特定医学应用（如模拟系统、手术干预和教育目的）的适用性。该研究探讨了来自广泛研究文章的见解，涵盖了不同的方法和观点。这篇综述阐明了未来研究的潜在方向，并有助于在理解人体骨骼骨折模式的获取和呈现方面取得进展。
