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开发和验证机器学习算法,以利用磁共振图像进行结性脊髓炎的早期检测

Emre Canayaz¹, Zehra Aysun Altikardes², Alparslan Unsal³

¹Vocational School of Technical Sciences, Marmara University, Istanbul, Türkiye.

Technology and health care : official journal of the European Society for Engineering and Medicine

|May 7, 2025

概括

机器学习模型使用短陶逆转恢复 (STIR) 核磁共振扫描精确检测结性脊髓炎 (AS). 这种方法有助于在慢性炎症疾病的早期诊断和治疗规划.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能的人工智能

背景情况:

化脊柱炎 (AS) 是一种慢性炎症疾病,影响脊柱和关节.
早期诊断和治疗对于预防残疾至关重要.

研究的目的:

开发和验证用于AS检测的机器学习 (ML) 算法.
仅使用短陶倒置恢复 (STIR) 磁共振 (MR) 图像.

主要方法:

从MRI图像中生成灰度共发生矩阵 (GLCM).
计算Haralick特征和列车ML模型.
使用696张MRI图像 (348AS+,348AS-),具有10倍的交叉验证和单独的验证集.

主要成果:

在验证中,ML模型取得了高性能 (精度:0.885,AUC:0.941).
与之前的基于GLCM的ML检测模型相比,表现出卓越的性能.

结论:

引入了一种新的ML方法来检测AS使用STIRMR图像.
解决了关于AS检测中ML模型可解释性的文献缺乏问题.
提供了早期和准确的AS诊断的有效策略,促进及时干预.

关键词:

短tau倒置恢复序列的恢复序列决策支持系统决策支持系统灰色级别的同时发生矩阵.图像的分类图像的分类.机器学习是机器学习.磁共振成像技术的使用

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