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:
影响因子:
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通讯作者:
Junan Yan
中科院分区:
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
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.