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Assessment of skin inflammation using near-infrared Raman spectroscopy combined with artificial intelligence analysis in an animal model
Analyst ( IF 3.6 ) Pub Date : 2022-05-27 , DOI: 10.1039/d2an00193d Yohei Kanemura 1, 2 , Meiko Kanazawa 1, 3 , Satoru Hashimoto 4 , Yuri Hayashi 1, 3 , Erina Fujiwara 4 , Ayako Suzuki 4 , Takashige Ishii 5 , Masakazu Goto 5 , Hiroshi Nozaki 5 , Takanori Inoue 4 , Hiroki Takanari 1
Analyst ( IF 3.6 ) Pub Date : 2022-05-27 , DOI: 10.1039/d2an00193d Yohei Kanemura 1, 2 , Meiko Kanazawa 1, 3 , Satoru Hashimoto 4 , Yuri Hayashi 1, 3 , Erina Fujiwara 4 , Ayako Suzuki 4 , Takashige Ishii 5 , Masakazu Goto 5 , Hiroshi Nozaki 5 , Takanori Inoue 4 , Hiroki Takanari 1
Affiliation
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Raman spectroscopy is a powerful method for estimating the molecular structure of a target that can be adapted for biomedical analysis given its non-destructive nature. Inflammatory skin diseases impair the skin's barrier function and interfere with the patient's quality of life. There are limited methods for non-invasive and objective assessment of skin inflammation. We examined whether Raman spectroscopy can be used to predict skin inflammation with high sensitivity and specificity when combined with artificial intelligence (AI) analysis. Inflammation was chemically induced in mouse ears, and Raman spectra induced by a 785 nm laser were recorded. A principal component (PC) analysis of the Raman spectra was performed to extract PCs with the highest percentage of variance and to estimate the statistical score. The accuracy in predicting inflammation based on the Raman spectra with or without AI analysis was assessed using receiver operating characteristic (ROC) curves. We observed some typical changes in the Raman spectra upon skin inflammation, which may have resulted from vasodilation and interstitial oedema. The estimated statistical scores based on spectral changes correlated with the histopathological changes in the skin. The ROC curve based on PC2, which appeared to include some spectral features, revealed a maximum accuracy rate of 80.0% with an area under the curve (AUC) of 0.864. The AI analysis improved the accuracy rate to 93.1% with an AUC of 0.972. The current findings demonstrate that the combination of Raman spectroscopy with near-infrared excitation and AI analysis can provide highly accurate information on the pathology of skin inflammation.
中文翻译：
在动物模型中使用近红外拉曼光谱结合人工智能分析评估皮肤炎症
拉曼光谱是一种强大的方法，用于估计目标的分子结构，鉴于其非破坏性，可适用于生物医学分析。炎症性皮肤病损害皮肤的屏障功能并干扰患者的生活质量。对皮肤炎症进行非侵入性和客观评估的方法有限。我们检查了拉曼光谱在与人工智能 (AI) 分析相结合时是否可用于以高灵敏度和特异性预测皮肤炎症。在小鼠耳朵中化学诱导炎症，并记录由 785 nm 激光诱导的拉曼光谱。对拉曼光谱进行主成分 (PC) 分析以提取具有最高方差百分比的 PC 并估计统计分数。使用接受者操作特征 (ROC) 曲线评估基于拉曼光谱（有或没有 AI 分析）预测炎症的准确性。我们观察到皮肤炎症时拉曼光谱的一些典型变化，这可能是由血管舒张和间质水肿引起的。基于与皮肤组织病理学变化相关的光谱变化的估计统计分数。基于 PC2 的 ROC 曲线似乎包含一些光谱特征，其最大准确率为 80.0%，曲线下面积 (AUC) 为 0.864。AI 分析将准确率提高到 93.1%，AUC 为 0.972。
更新日期：2022-05-27
中文翻译：
在动物模型中使用近红外拉曼光谱结合人工智能分析评估皮肤炎症
拉曼光谱是一种强大的方法，用于估计目标的分子结构，鉴于其非破坏性，可适用于生物医学分析。炎症性皮肤病损害皮肤的屏障功能并干扰患者的生活质量。对皮肤炎症进行非侵入性和客观评估的方法有限。我们检查了拉曼光谱在与人工智能 (AI) 分析相结合时是否可用于以高灵敏度和特异性预测皮肤炎症。在小鼠耳朵中化学诱导炎症，并记录由 785 nm 激光诱导的拉曼光谱。对拉曼光谱进行主成分 (PC) 分析以提取具有最高方差百分比的 PC 并估计统计分数。使用接受者操作特征 (ROC) 曲线评估基于拉曼光谱（有或没有 AI 分析）预测炎症的准确性。我们观察到皮肤炎症时拉曼光谱的一些典型变化，这可能是由血管舒张和间质水肿引起的。基于与皮肤组织病理学变化相关的光谱变化的估计统计分数。基于 PC2 的 ROC 曲线似乎包含一些光谱特征，其最大准确率为 80.0%，曲线下面积 (AUC) 为 0.864。AI 分析将准确率提高到 93.1%，AUC 为 0.972。
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