Ensemble multivariate analysis to improve identification of articular cartilage disease in noisy Raman spectra.
Ensemble multivariate analysis to improve identification of articular cartilage disease in noisy Raman spectra.
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DOI：
10.1002/jbio.201300200
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发表时间：
2015-07
影响因子：
2.8
通讯作者：
Evseenko, Denis
中科院分区：
文献类型：
--
作者：
Richardson, Wade;Wilkinson, Dan;Wu, Ling;Petrigliano, Frank;Dunn, Bruce;Evseenko, Denis
软骨疾病早期诊断新方法的发展可以显著改善患者的护理。拉曼光谱是一种新兴的生物医学技术，具有识别疾病组织的独特潜力，尽管难以获得诊断所需的样本，并且过多的信号噪声可能会减缓其向临床工具的发展。在当前的报告中，我们详细介绍了主成分分析-线性判别分析（PCA-LDA）对软骨疾病建模材料对光谱的使用，以创建多个光谱评分指标，这可以限制对低信噪比（SNR）拉曼光谱中识别疾病的主要训练数据的依赖。我们的概念验证实验表明，这些模型指标的组合有可能改善来自人类正常和骨关节炎（OA）软骨的低信噪比拉曼光谱的分类，而不是使用来自相同健康组织和OA组织的光谱训练的单一指标。散点图显示了来自健康（蓝色）和骨关节炎（红色）软骨的7656个低信噪比光谱的PCA-LDA衍生的人类疾病度量评分与大鼠模型度量评分。竖线和水平线代表优化后的单度量分类边界。深色对角线表示两个指标优化组合后的边界分类。缩写：er（错误率），PCA-LDA（主成分分析-线性判别分析），HOA（人骨关节炎），HAC（人关节软骨），RIF（大鼠损伤纤维软骨），RAC（大鼠关节软骨）。
The development of new methods for the early diagnosis of cartilage disease could offer significant improvement in patient care. Raman spectroscopy is an emerging biomedical technology with unique potential to recognize disease tissues, though difficulty in obtaining the samples needed to train a diagnostic and excessive signal noise could slow its development into a clinical tool. In the current report we detail the use of principal component analysis – linear discriminant analysis (PCA-LDA) on spectra from pairs of materials modeling cartilage disease to create multiple spectral scoring metrics, which could limit the reliance on primary training data for identifying disease in low signal-to-noise-ratio (SNR) Raman spectra. Our proof-of-concept experiments show that combinations of these model-metrics has the potential to improve the classification of low-SNR Raman spectra from human normal and osteoarthritic (OA) cartilage over a single metric trained with spectra from the same healthy and OA tissues. Scatter plot showing the PCA-LDA derived human-disease-metric scores versus rat-model-metric scores for 7656 low signal-to-noise spectra from healthy (blue) and osteoarthritic (red) cartilage. Light vertical and horizontal lines represent the optimized single metric classification boundary. Dark diagonal line represents the classification of boundary resulting from the optimized combination of the two metrics. Abbreviations: er (error rate), PCA-LDA (principal component analysis – linear discriminant analysis), HOA (human osteoarthritis), HAC (human articular cartilage), RIF (rat injury fibrocartilage), RAC (rat articular cartilage).
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影响因子：
2.5
作者：
Krishna, Hemant;Majumder, Shovan K.;Gupta, Pradeep K.
通讯作者：
Gupta, Pradeep K.
影响因子：
5.1
作者：
Attur M;Krasnokutsky-Samuels S;Samuels J;Abramson SB
通讯作者：
Abramson SB
影响因子：
7
作者：
Eltawil NM;De Bari C;Achan P;Pitzalis C;Dell'accio F
通讯作者：
Dell'accio F
影响因子：
2.9
作者：
FRUSHOUR, BG;KOENIG, JL
通讯作者：
KOENIG, JL
DOI：
10.1039/c0an00824a
发表时间：
2011-04-21
期刊：
The Analyst
影响因子：
--
作者：
Esmonde-White KA;Esmonde-White FW;Morris MD;Roessler BJ
通讯作者：
Roessler BJ