Autofluorescence spectral analysis for detecting urinary stone composition in emulated intraoperative ambient.

Autofluorescence spectral analysis for detecting urinary stone composition in emulated intraoperative ambient.
复制标题

自体荧光光谱分析用于检测模拟术中环境中的尿路结石成分。

DOI:
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发表时间:
2023
期刊:
Spectrochimica Acta Part A - Molecular and Biomolecular Spectroscopy
影响因子:
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通讯作者:
Junan Yan
Junan Yan
中科院分区:
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文献类型:
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作者:
Xing Li;Siji Song;Jiwei Yao;Xiang Liao;Min Chen;Jinliang Zhai;Lang Lang;Chun;Na Zhang;Chunhui Yuan;Chun;Hui Li;Xiaojun Wu;Jing Lin;Chunlian Li;Yan Wang;Jing Lyu;Min Li;Zhenqiao Zhou;Mengke Yang;Hong;Junan Yan

文献摘要

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近十年来,尿石症的患病率和疾病负担在全球范围内大幅增加,腔内钬激光碎石术已成为主要的治疗方法。然而,不适当的激光能量设置会增加围手术期并发症的风险,这主要是由于术中缺乏关于结石成分的信息,而结石成分决定了结石的熔点。为了解决这个问题,我们开发了一种基于纤维的荧光光谱法,该方法检测并将尿路结石的自身荧光光谱指纹分为三类:草酸钙、尿酸和鸟粪石。应用支持向量机(SVM)对钙结石与非钙结石、尿酸与鸟粪石分类的预测准确率分别达到90.28%和96.70%。在模拟的术中环境中,在纤维尖端和结石表面之间的大范围工作距离和角度上实现了高精度和特异性。我们的工作为设计一种临床设备奠定了方法学基础,该设备可实现实时、原位尿路结石分类,以优化激光消融参数并减少碎石术围手术期并发症。
The prevalence and disease burden of urolithiasis has increased substantially worldwide in the last decade, and intraluminal holmium laser lithotripsy has become the primary treatment method. However, inappropriate laser energy settings increase the risk of perioperative complications, largely due to the lack of intraoperative information on the stone composition, which determines the stone melting point. To address this issue, we developed a fiber-based fluorescence spectrometry method that detects and classifies the autofluorescence spectral fingerprints of urinary stones into three categories: calcium oxalate, uric acid, and struvite. By applying the support vector machine (SVM), the prediction accuracy achieved 90.28 % and 96.70% for classifying calcium stones versus non-calcium stones and uric acid versus struvite, respectively. High accuracy and specificity were achieved for a wide range of working distances and angles between the fiber tip and stone surface in an emulated intraoperative ambient. Our work establishes the methodological basis for engineering a clinical device that achieves real-time, in situ classification of urinary stones for optimizing the laser ablation parameters and reducing perioperative complications in lithotripsy.