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

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 300; p. 122913
• Main Authors: Li, Xing, Song, Siji, Yao, Jiwei, Liao, Xiang, Chen, Min, Zhai, Jinliang, Lang, Lang, Lin, Chunyan, Zhang, Na, Yuan, Chunhui, Li, Chunxia, Li, Hui, Wu, Xiaojun, Lin, Jing, Li, Chunlian, Wang, Yan, Lyu, Jing, Li, Min, Zhou, Zhenqiao, Yang, Mengke, Jia, Hongbo, Yan, Junan
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 05.11.2023
• Subjects: Autofluorescence spectrum; Humans; Lithotripsy, Laser; Machine learning; Struvite; Uric Acid - analysis; Urinary Calculi - chemistry; Urinary Calculi - surgery; Urinary stone detection in physiological condition; Urolithiasis; Urolithiasis; Autofluorescence spectrum; Urinary stone detection in physiological condition; Machine learning
• ISSN: 1386-1425; 1873-3557
• DOI: 10.1016/j.saa.2023.122913

[Display omitted] •Autofluorescence signals of urinary stones exhibit spectral fingerprints of stone composition.•High accuracy and specificity of urinary stone classification achieved in an emulated intraoperative ambient by fiber-based fluorescence spectroscopy.•The result enables a device for real-time intraoperative classification of urinary stones in laser lithotripsy surgery. 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.