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通过深度学习方法对糖尿病足骨髓炎和Charcot神经病变骨关节病的差异诊断

Maide Cakir¹, Gökalp Tulum², Ferhat Cuce³

¹Department of Electrical Engineering, Faculty of Engineering and Natural Sciences, Bandirma Onyedi Eylul University, Balikesir, Turkey. maidecakrr@gmail.com.

Journal of imaging informatics in medicine

|March 16, 2024

概括

深度学习算法准确地区分糖尿病脚的骨髓信号强度. 这些AI模型可以区分骨质炎,Charcot神经病性骨关节炎和创伤,帮助诊断.

科学领域:

放射学和医学成像学医学成像学
人工智能在医学中的应用
整形外科整形外科整形外科

背景情况:

区分骨髓信号强度对于诊断骨髓炎 (OM),Charcot神经病性骨关节炎 (CNO) 和糖尿病足部创伤 (TR) 等疾病至关重要.
传统方法可能具有侵入性或缺乏特异性,需要先进的诊断工具.

研究的目的:

评估深度学习 (DL) 算法的有效性,以区分与OM,CNO和TR相关的骨髓信号强度模式.
为了比较ResNet-50和EfficientNet-b0模型在此分类任务中的性能.

主要方法:

一项回顾性研究涉及148名患者,从T1加权和T2加权MRI图像中标记了骨髓区域.
利用ResNet-50和EfficientNet-b0模型的多类分类 (MCC) 和二进制类分类 (BCC) 方法.
对每个分类模型和图像加权的计算精度,灵敏度和特异性.

主要成果:

EfficientNet-b0实现了最高的准确性 (T1上的97.1%,T2上的96.8%).
对EfficientNet-b0的敏感度在93.2%至98.1% (T1) 和95.6%至98.6% (T2) 之间.
在所有条件和图像类型中,EfficientNet-b0的特异性值超过了98.4%.

结论:

深度学习算法,特别是EfficientNet-b0,在根据骨髓信号强度区分OM,CNO和TR方面表现出高精度.
DL提供了一种有前途的非侵入性工具,用于诊断糖尿病足的这些疾病.
这些发现支持将人工智能纳入糖尿病足并发症的放射性评估.

关键词:

查科特神经病变骨关节病变是一种神经病变骨关节病.深度学习是一种深度学习.糖尿病足患者的糖尿病足.骨髓炎是一种骨质炎.

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