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快速查自身免疫性疾病使用福里埃变换红外光谱和深度学习算法

Xue Wu^1,2, Wei Shuai³, Chen Chen⁴

¹Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China.

Frontiers in immunology

|January 1, 2024

概括

这项研究引入了一种使用福里埃变换红外光谱和深度学习的新方法,用于快速,非侵入性诊断结性脊髓炎 (AS),类风湿性关节炎 (RA) 和骨关节炎 (OA). 该MSResNet模型实现了87%的准确性,为这些类风湿性疾病提供了一个有前途的辅助诊断工具.

科学领域:

生物医学工程生物医学工程
医学诊断医学诊断医学诊断
计算生物学计算生物学

背景情况:

化脊柱炎 (AS),类风湿性关节炎 (RA) 和骨关节炎 (OA) 是类风湿性免疫疾病,如果不治疗,可能导致严重的关节破坏和残疾.
早期诊断和治疗对于改善患者的治疗结果和风湿性免疫疾病的预后至关重要.
目前的诊断方法可能缺乏及时干预所需的速度和准确性.

研究的目的:

开发一种快速,非侵入性和准确的方法来区分AS,RA,OA和健康个体.
为了评估福里埃变换红外光谱法 (FTIR) 与疾病诊断的深度学习模型相结合的有效性.
根据血清光谱数据,确定最佳的深度学习模型来区分这些类风湿病.

主要方法:

从320名个人 (每人80名AS,RA,OA和健康对照) 的血清样本使用富里埃变换红外光谱 (FTIR) 在700-4000厘米-1范围内进行分析.
使用机器学习算法开发了四种深度学习模型 (AlexNet,ResNet,MSCNN,MSResNet) 来分类FTIR光谱数据.
包含多尺度卷积模块和残余块的MSResNet模型是专门设计的,以增强特征提取和减少光谱噪声.

主要成果:

血清FTIR光谱学揭示了与蛋白质和脂质成分相关的特征性光谱峰值.
与AlexNet,ResNet和MSCNN相比,MSResNet深度学习模型表现出更高的性能.
在区分四个组的过程中,MSResNet模型实现了0.87的最高诊断准确度.

结论:

血清FTIR光谱与深度学习算法相结合,为AS,RA和OA提供了可行的和有效的辅助诊断方法.
这种方法可以使这些类风湿性免疫疾病的非侵入性,快速和准确的分化.
这些发现强调了先进的计算方法在改善风湿病的早期诊断和管理方面的潜力.

关键词:

里埃变换红外光谱法红外光谱法结性脊柱炎是一种疾病.深度学习是一种深度学习.诊断诊断诊断诊断诊断诊断多层次的核聚变技术骨关节炎是一种关节炎.类风湿性关节炎类风湿性关节炎

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