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

Copyright © 2023 Elsevier B.V. All rights reserved..

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.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:300

Enthalten in:

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy - 300(2023) vom: 05. Nov., Seite 122913

Sprache:

Englisch

Beteiligte Personen:

Li, Xing [VerfasserIn]
Song, Siji [VerfasserIn]
Yao, Jiwei [VerfasserIn]
Liao, Xiang [VerfasserIn]
Chen, Min [VerfasserIn]
Zhai, Jinliang [VerfasserIn]
Lang, Lang [VerfasserIn]
Lin, Chunyan [VerfasserIn]
Zhang, Na [VerfasserIn]
Yuan, Chunhui [VerfasserIn]
Li, Chunxia [VerfasserIn]
Li, Hui [VerfasserIn]
Wu, Xiaojun [VerfasserIn]
Lin, Jing [VerfasserIn]
Li, Chunlian [VerfasserIn]
Wang, Yan [VerfasserIn]
Lyu, Jing [VerfasserIn]
Li, Min [VerfasserIn]
Zhou, Zhenqiao [VerfasserIn]
Yang, Mengke [VerfasserIn]
Jia, Hongbo [VerfasserIn]
Yan, Junan [VerfasserIn]

Links:

Volltext

Themen:

268B43MJ25
AW3EJL1462
Autofluorescence spectrum
Journal Article
Machine learning
Struvite
Uric Acid
Urinary stone detection in physiological condition
Urolithiasis

Anmerkungen:

Date Completed 19.06.2023

Date Revised 19.06.2023

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1016/j.saa.2023.122913

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM357650514