Preview

Zhurnal Prikladnoii Spektroskopii

Advanced search
Open Access Open Access  Restricted Access Subscription Access

Discriminant Analysis of Kidney Stone Types Based on Laser-Induced Fluorescence Spectroscopy

Abstract

Accurate identification of kidney stone compositions is critical for optimizing clinical diagnoses and formulating individualized treatment plans. This study developed a rapid, noninvasive method for classifying kidney stone types via laser-induced fluorescence (LIF) spectroscopy combined with machine learning, establishing a 405 nm laser-based LIF system to collect spectral data for four common stone types: calcium oxalate monohydrate, anhydrous uric acid, magnesium ammonium phosphate hexahydrate, and carbonate apatite. Spectral data were preprocessed using standard normal variate transformation and normalization to reduce noise and morphological variability. Following feature selection and scaling, four classification models  (e.g., K-nearest neighbors, support vector classifier, random forest, and eXtreme gradient boosting) were constructed, with hyperparameters optimized via a Bayesian algorithm. All models performed well on an independent test set, with the support vector classifier achieving the highest average accuracy of 92%. This proposed technique enables reliable and efficient identification of the intraoperative stone components and holds significant potential for enhancing clinical diagnostics, guiding personalized treatment, and supporting recurrence prevention.

About the Authors

Feifan Shi
School of Physics, Anhui University; Anhui Province Key Laboratory of Photonic Devices and Materials
China

Hefei



Huadong Wang
Anhui Province Key Laboratory of Photonic Devices and Materials
China

Hefei



Saifullah Jamali
Anhui Province Key Laboratory of Photonic Devices and Materials
China

Hefei



Hongbo Fu
Anhui Province Key Laboratory of Photonic Devices and Materials; Hefei Institute of Technology Innovation
China

Hefei



Daming Wang
Second Affiliated Hospital of Anhui Medical University
China

Hefei



Zongling Ding
School of Physics, Anhui University
China

Hefei



References

1. T. Alelign, B. Petros, Adv. Urol., 1–12 (2018), https://doi.org/10.1155/2018/3068365.

2. A. D. Rule, J. C. Lieske, X. Li, et al., J. Am. Soc. Nephrol., 25, 2878–2886 (2014). https://doi.org/10.1681/ASN.2013091011.

3. S. Chen, L. Zhu, S. Yang, et al., Urology, 79, 293–297 (2012), https://doi.org/10.1016/j.urology.2011.08.036.

4. Z. Gu, J. Qi, H. Shen, et al., Lasers Med. Sci., 25, 577–580 (2010), https://doi.org/10.1007/s10103-010-0769-x.

5. G. Xiang, J. Chen, D. Ho, et al., Ultrason. Sonochem., 101, 106649 (2023), https://doi.org/10.1016/j.ultsonch.2023.106649.

6. N. Harada, J. Yatsuda, R. Kurahashi, et al., IJU Case Rep., 5, 281–285 (2022), https://doi.org/10.1002/iju5.12464.

7. B. Turna, R. J. Stein, M. C. Smaldone, et al., J. Urol., 179, 1415–1419 (2008). https://doi.org/10.1016/j.juro.2007.11.076

8. J. W. He, J. Int. Med. Res., 52, 3000605241275333 (2024), https://doi.org/10.1177/03000605241275333.

9. T. Bach, B. Geavlete, T. R. W. Herrmann, A. J. Gross, J. Endourol., 22, 1639–1643 (2008). https://doi.org/10.1089/end.2008.0184.

10. F. Pasqui, F. Dubosq, K. Tchala, et al., Eur. Urol., 45, 58–64 (2004), https://doi.org/10.1016/j.eururo.2003.08.013

11. M. Wang, Q. Shao, X. Zhu, et al., Urol. Int., 105, 587–593 (2021), https://doi.org/10.1159/000512054.

12. R. L. Blackmon, P. B. Irby, N. M. Fried, J. Biomed. Opt., 16, 71403 (2011), https://doi.org/10.1117/1.3564884.

13. M. Koch, M. Schapher, K. Mantsopoulos, H. Iro, Lasers Surg. Med., 53, 488–498 (2021), https://doi.org/10.1002/lsm.23325.

14. P. Kronenberg, O. Traxer, World J. Urol., 33, 463–469 (2015), https://doi.org/10.1007/s00345-014-1395-1.

15. A. Hertel, M. F. Froelich, D. Overhoff, et al., Eur. Radio, 35, 3120–3130 (2025), https://doi.org/10.1007/s00330024-11262-w.

16. A. H. Khan, S. Imran, J. Talati, L. Jafri, Clin. Urol., 59, 32 (2018), https://doi.org/10.4111/icu.2018.59.1.32.

17. W. Sofińska-Chmiel, M. Goliszek, M. Drewniak, et al., Mol. (basel Switz), 28, 6089 (2023), https://doi.org/10.3390/molecules28166089.

18. M. M. E. L. Henderickx, S. J. M. Stoots, D. M. De Bruin, et al., J. Endourol., 36, 1362–1370 (2022), https://doi.org/10.1089/end.2022.0217.

19. M. Kwaśny, A. Bombalska, Sens. (basel Switz), 22, 2956 (2022), https://doi.org/10.3390/s22082956.

20. Y. H. El-Sharkawy, S. Elbasuney, Photodiagn. Photodyn. Ther., 40, 103186 (2022), https://doi.org/10.1016/j.pdpdt.2022.103186.

21. R. Du, D. Yang, X. Yin, Sens. (basel Switz), 22, 1168 (2022), https://doi.org/10.3390/s22031168.

22. B. Lange, D. Jocham, R. Brinkmann, J. Cordes, Lasers Surg. Med., 49, 361–365 (2017), https://doi.org/10.1002/lsm.22611.

23. X. Li, Autofluorescence Spectral Analysis for Detecting Urinary Stone Composition in Emulated Intraoperative Ambient (2023).

24. I. Saini, D. Singh, A. Khosla, J. Adv. Res., 4, 331–344 (2013), https://doi.org/10.1016/j.jare.2012.05.007.

25. M. Awad, R. Khanna, In: Efficient Learning Machines, Berkeley, CA, 39–66 (2015).

26. R. Genuer, J.-M. Poggi, C. Tuleau-Malot, Pattern Rec. Lett., 31, 2225–2236 (2010), https://doi.org/10.1016/j.patrec.2010.03.014.

27. T. Chen, C. Guestrin, In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, San Francisco California USA, 785–794 (2016).


Review

For citations:


Shi F., Wang H., Jamali S., Fu H., Wang D., Ding Z. Discriminant Analysis of Kidney Stone Types Based on Laser-Induced Fluorescence Spectroscopy. Zhurnal Prikladnoii Spektroskopii. 2026;93(5):722.

Views: 3

JATS XML

ISSN 0514-7506 (Print)