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在使用YOLOv8和先进的数据增强技术的X射线图像中对AO/OTA 31A/B大腿骨折的分类

Giorgia Marullo¹, Luca Ulrich¹, Francesca Giada Antonaci¹

¹Department of Management, Production, and Design, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino 10129, Italy.

|September 26, 2024

概括

这项研究使用YOLOv8深度学习增强了股骨骨折分类,提高了诊断准确度,以获得更好的患者护理. 这种先进的模型有助于医生及时准确地识别骨折.

科学领域:

整形外科整形外科整形外科
医疗成像医学成像
人工智能的人工智能

背景情况:

股骨骨折是一个重大的全球健康挑战,由于高发病率,发病率和死亡率,影响患者和家属.
计算机辅助诊断 (CAD) 技术,特别是深度学习 (DL),在提高骨折分类准确度方面显示出前景.
在训练DL模型的数据采集和解释其复杂发现方面仍然存在挑战.

研究的目的:

为了提高腿骨骨折分类的准确性和可解释性,使用DL.
改进现有的基于DL的Osteosynthesefragen和骨科创伤协会 (AO/OTA) 系统分类.
支持医生做出准确及及时的患者护理决策.

主要方法:

使用了最先进的YOLOv8架构,为解释性进行了微调.
在预处理过程中采用数据增强技术,通过图像处理更改来扩展数据集.
评估模型在股骨骨折分类任务上的表现.

主要成果:

微调的YOLOv8模型实现了高性能指标:0.9准确度,0.85精度,0.85回忆率和0.85F1得分 (各类平均).
证明有效提高OTA/AO系统的股骨骨折分类.
在改进过程中,模型的可解释性是关键焦点.

结论:

提出的YOLOv8架构有效地改善了股骨骨折的分类.
这种方法有助于医生实现快速和准确的诊断.
该研究强调了精细的DL模型在骨科创伤护理中的潜力.

关键词:

计算机辅助诊断是一种计算机辅助诊断.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.股骨骨折股骨骨折股骨骨折

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相关概念视频

Classification of Bones

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...