多源数据变异自编码器的全球和本地特征脱:通过多源拉曼光谱融合技术来诊断Igan的可解释模型
Wei Shuai1, Xuecong Tian2, Enguang Zuo2
1College of Software, Xinjiang University, Urumqi 830046, China.
Artificial intelligence in medicine
|December 19, 2024
概括
这项研究引入了一种使用拉曼光谱的新方法,以增强数据和诊断免疫球蛋白A脏病 (IgAN). 开发的模型在从血清和尿液光谱中检测IGAN时实现了高精度.
科学领域:
- 生物医学光谱学 生物医学光谱学
- 数据融合和数据分析.
- 医学诊断 医学诊断 医学诊断
背景情况:
- 拉曼光谱从单个光谱提供了有限的分子洞察力.
- 血清和尿液拉曼光谱的有效融合可以产生更丰富的信息.
- 目前使用拉曼光谱的免疫球蛋白A脏病 (IgAN) 研究受到小样本大小和低信号噪声比的影响,这可能会阻碍直接多源数据融合的诊断准确性.
研究的目的:
- 开发数据增强和光谱优化方法,以提高拉曼光谱质量和样本大小.
- 使用多源拉曼光谱数据构建一个新的脱变异自编码器模型,用于使用多源拉曼光谱数据准确的Igan诊断.
- 通过可解释的分析来确定IGAN诊断的关键生物标志物.
主要方法:
- 提出了一种基于自编码器的变异性数据增强方法,将样本大小翻一番,并改善信号噪声比.
- 开发了一个脱变异自编码器模型 (DMSGL-VAE) 来整合来自多源拉曼光谱的全球和本地特征.
- 利用跨源重建损失和脱损失用于模型约束和验证的脱有效性.
- 采用夏普利添加剂解释 (SHAP) 进行诊断特征的可解释分析.
主要成果:
- 在DMSGL-VAE模型中,在试验组上,Igan诊断的曲线下的高面积 (AUC) 为0.9958.
- 数据增强成功地使样本大小增加了一倍,并改善了光谱信号噪声比.
- 在SHAP分析中,蛋白质,基酸盐和瓜被确定为血清和尿液拉曼光谱中Igan的潜在常见生物标志物.
结论:
- DMSGL-VAE模型使用拉曼光谱学证明了快速,非侵入性和准确的Igan查能力.
- 数据增强和光谱优化方法有效地解决了小样本大小和低信号噪声比的局限性.
- 对生物标志物的可解释性分析可以帮助理解Igan,并开发改进的诊断策略.
相关概念视频
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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
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