PDF(Pubmed)
Abstract：
BACKGROUND: Impaired microcirculation is a cornerstone of sepsis development and leads to reduced tissue oxygenation, influenced by fluid and catecholamine administration during treatment. Hyperspectral imaging (HSI) is a non-invasive bedside technology for visualizing physicochemical tissue characteristics. Machine learning (ML) for skin HSI might offer an automated approach for bedside microcirculation assessment, providing an individualized tissue fingerprint of critically ill patients in intensive care. The study aimed to determine if machine learning could be utilized to automatically identify regions of interest (ROIs) in the hand, thereby distinguishing between healthy individuals and critically ill patients with sepsis using HSI.
METHODS: HSI raw data from 75 critically ill sepsis patients and from 30 healthy controls were recorded using TIVITA® Tissue System and analyzed using an automated ML approach. Additionally, patients were divided into two groups based on their SOFA scores for further subanalysis: less severely ill (SOFA ≤ 5) and severely ill (SOFA > 5). The analysis of the HSI raw data was fully-automated using MediaPipe for ROI detection (palm and fingertips) and feature extraction. HSI Features were statistically analyzed to highlight relevant wavelength combinations using Mann-Whitney-U test and Benjamini, Krieger, and Yekutieli (BKY) correction. In addition, Random Forest models were trained using bootstrapping, and feature importances were determined to gain insights regarding the wavelength importance for a model decision.
RESULTS: An automated pipeline for generating ROIs and HSI feature extraction was successfully established. HSI raw data analysis accurately distinguished healthy controls from sepsis patients. Wavelengths at the fingertips differed in the ranges of 575-695 nm and 840-1000 nm. For the palm, significant differences were observed in the range of 925-1000 nm. Feature importance plots indicated relevant information in the same wavelength ranges. Combining palm and fingertip analysis provided the highest reliability, with an AUC of 0.92 to distinguish between sepsis patients and healthy controls.
CONCLUSIONS: Based on this proof of concept, the integration of automated and standardized ROIs along with automated skin HSI analyzes, was able to differentiate between healthy individuals and patients with sepsis. This approach offers a reliable and objective assessment of skin microcirculation, facilitating the rapid identification of critically ill patients.
METHODS: HSI raw data from 75 critically ill sepsis patients and from 30 healthy controls were recorded using TIVITA® Tissue System and analyzed using an automated ML approach. Additionally, patients were divided into two groups based on their SOFA scores for further subanalysis: less severely ill (SOFA ≤ 5) and severely ill (SOFA > 5). The analysis of the HSI raw data was fully-automated using MediaPipe for ROI detection (palm and fingertips) and feature extraction. HSI Features were statistically analyzed to highlight relevant wavelength combinations using Mann-Whitney-U test and Benjamini, Krieger, and Yekutieli (BKY) correction. In addition, Random Forest models were trained using bootstrapping, and feature importances were determined to gain insights regarding the wavelength importance for a model decision.
RESULTS: An automated pipeline for generating ROIs and HSI feature extraction was successfully established. HSI raw data analysis accurately distinguished healthy controls from sepsis patients. Wavelengths at the fingertips differed in the ranges of 575-695 nm and 840-1000 nm. For the palm, significant differences were observed in the range of 925-1000 nm. Feature importance plots indicated relevant information in the same wavelength ranges. Combining palm and fingertip analysis provided the highest reliability, with an AUC of 0.92 to distinguish between sepsis patients and healthy controls.
CONCLUSIONS: Based on this proof of concept, the integration of automated and standardized ROIs along with automated skin HSI analyzes, was able to differentiate between healthy individuals and patients with sepsis. This approach offers a reliable and objective assessment of skin microcirculation, facilitating the rapid identification of critically ill patients.
摘要：
背景：微循环受损是脓毒症发展的基石，并导致组织氧合降低，治疗期间受液体和儿茶酚胺给药的影响。高光谱成像（HSI）是一种用于可视化物理化学组织特征的无创床边技术。皮肤HSI的机器学习（ML）可能为床边微循环评估提供一种自动化方法，提供重症监护重症患者的个性化组织指纹。该研究旨在确定是否可以利用机器学习来自动识别手中的感兴趣区域(ROI)。从而区分健康个体和使用HSI的脓毒症危重患者。
方法：使用TIVITA®TissueSystem记录75例重症脓毒症患者和30例健康对照的HSI原始数据，并使用自动化ML方法进行分析。此外,根据SOFA评分将患者分为两组进行进一步亚分析:病情较轻(SOFA≤5)和病情较重(SOFA>5).HSI原始数据的分析是使用MediaPipe进行ROI检测（手掌和指尖）和特征提取的全自动分析。使用Mann-Whitney-U检验和Benjamini对HSI特征进行统计分析，以突出相关波长组合，克里格,和Yekutieli（BKY）更正。此外,随机森林模型使用自举训练，并确定了特征重要性，以获得有关波长重要性的见解，以用于模型决策。
结果：成功建立了用于生成ROI和HSI特征提取的自动化管道。HSI原始数据分析可准确区分健康对照与败血症患者。指尖的波长在575-695nm和840-1000nm的范围内不同。对于手掌，在925-1000nm的范围内观察到显着差异。特征重要性图指示相同波长范围内的相关信息。结合手掌和指尖分析提供了最高的可靠性，AUC为0.92以区分败血症患者和健康对照。
结论：基于这一概念证明，自动化和标准化ROI与自动化皮肤HSI分析的集成，能够区分健康个体和脓毒症患者。这种方法提供了皮肤微循环的可靠和客观的评估，有助于快速识别危重病人。
方法：使用TIVITA®TissueSystem记录75例重症脓毒症患者和30例健康对照的HSI原始数据，并使用自动化ML方法进行分析。此外,根据SOFA评分将患者分为两组进行进一步亚分析:病情较轻(SOFA≤5)和病情较重(SOFA>5).HSI原始数据的分析是使用MediaPipe进行ROI检测（手掌和指尖）和特征提取的全自动分析。使用Mann-Whitney-U检验和Benjamini对HSI特征进行统计分析，以突出相关波长组合，克里格,和Yekutieli（BKY）更正。此外,随机森林模型使用自举训练，并确定了特征重要性，以获得有关波长重要性的见解，以用于模型决策。
结果：成功建立了用于生成ROI和HSI特征提取的自动化管道。HSI原始数据分析可准确区分健康对照与败血症患者。指尖的波长在575-695nm和840-1000nm的范围内不同。对于手掌，在925-1000nm的范围内观察到显着差异。特征重要性图指示相同波长范围内的相关信息。结合手掌和指尖分析提供了最高的可靠性，AUC为0.92以区分败血症患者和健康对照。
结论：基于这一概念证明，自动化和标准化ROI与自动化皮肤HSI分析的集成，能够区分健康个体和脓毒症患者。这种方法提供了皮肤微循环的可靠和客观的评估，有助于快速识别危重病人。
