Life Lens-AI: Advanced AI-Driven Framework for Predicting and Preventing Kidney Stone Recurrence with Personalized Care

Kidney stone disease continues to be a major global health challenge, largely due to its high recurrence rate even after successful surgical treatment. This study introduces LIFE Lens-AI, an intelligent framework designed to support both prediction and long-term prevention of kidney stone recurrence through the use of artificial intelligence.

The proposed system combines predictive analytics, medical image analysis, and personalized recommendation techniques within a single integrated platform. It leverages diverse data sources, including patient demographics, clinical history, lifestyle patterns, and medical imaging, to generate accurate and clinically meaningful insights.

The predictive module operates in two stages: recurrence risk prediction using XGBoost and surgical intervention prediction using a Random Forest classifier. The latter is modeled as a binary classification task, where labels are derived from established clinical treatment guidelines and historical decision patterns. Experimental results show that the system achieves an accuracy of 87.9% for recurrence prediction and 90.5% for surgical decision classification, supported by strong performance across precision, recall, F1-score, and AUC-ROC metrics.

Beyond prediction, the framework emphasizes patientcentric care by offering personalized dietary suggestions, specialist recommendations, and continuous monitoring support. The system is implemented as a secure webbased application with modern encryption and authentication mechanisms. Overall, LIFE Lens-AI provides a practical step toward proactive, AI-enabled healthcare and improved long-term patient outcomes.

Kidney stone analysis, prediction models, personalized treatment planning, machine learning, deep learning, artificial intelligence

1. E. Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York, NY, USA: Basic Books, 2019. [Google Scholar] [Crossref]

2. G. Hinton, Y. LeCun, and Y. Bengio, Deep Learning. Cambridge, MA, USA: MIT Press, 2012. [Google Scholar] [Crossref]

3. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016. [Google Scholar] [Crossref]

4. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015. [Google Scholar] [Crossref]

5. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. Int. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015, pp. 234–241. [Google Scholar] [Crossref]

6. D. Shen, G. Wu, and H. I. Suk, “Deep learning in medical image analysis,” Annual Review of Biomedical Engineering, vol. 19, pp. 221–248, 2017. [Google Scholar] [Crossref]

7. A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017. [Google Scholar] [Crossref]

8. F. Chollet, Deep Learning with Python. Shelter Island, NY, USA: [Google Scholar] [Crossref]

9. Manning Publications, 2017. [Google Scholar] [Crossref]

10. F. Mahmoodi, A. Andishgar, E. Mahmoudi, A. Monsef, S. Bazmi, and R. Tabrizi, “Predicting symptomatic kidney stones using machine learning algorithms,” BMC Research Notes, vol. 17, 2024. [Google Scholar] [Crossref]

11. A. Pimpalkar, R. Kulkarni, and S. Patil, “Fine-tuned deep learning models for early detection and classification of kidney diseases,” Scientific Reports, vol. 15, no. 1, 2025. [Google Scholar] [Crossref]

12. T. Yanase, Y. Kawahara, K. Ito, et al., “AI-driven prediction of renal stone recurrence following endoscopic combined intrarenal surgery,” Urology, vol. 187, pp. 45–52, 2025. [Google Scholar] [Crossref]

13. G. Zhu, C. Li, Y. Guo, L. Sun, T. Jin, Z. Wang, and F. Zhou, “Predicting stone composition via machine-learning models trained on intra-operative endoscopic digital images,” BMC Urology, vol. 24, 2024. [Google Scholar] [Crossref]

14. A. Abraham, N. L. Kavoussi, W. Sui, C. Bejan, J. A. Capra, and R. Hsi, “Machine learning prediction of kidney stone composition using electronic health records,” Journal of Endourology, vol. 36, no. 2, pp. 243–250, 2022. [Google Scholar] [Crossref]

15. D. C. Elton, E. B. Turkbey, P. J. Pickhardt, and R. M. Summers, “A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans,” Medical Physics, vol. 49, no. 4, pp. 2545–2554, 2022. [Google Scholar] [Crossref]

16. S. Verma and P. Sharma, “Non-invasive kidney stone prediction using machine learning: an extensive review,” Biomedical and Pharmacology Journal, vol. 18, no. 2, 2025. [Google Scholar] [Crossref]

17. M. Gulhane, R. Patil, and A. Deshmukh, “Integrative approach for efficient detection of kidney stones using machine learning,” Procedia Computer Science, vol. 235, pp. 152–160, 2024. [Google Scholar] [Crossref]

18. R. Kumar, Application of Deep Learning in Predicting Kidney Stone [Google Scholar] [Crossref]

19. Recurrence. Ph.D. dissertation, University of Delhi, New Delhi, India, 2023. [Google Scholar] [Crossref]

20. P. Sharma, Personalized Nutrition Strategies for Kidney Stone Prevention: A Machine Learning Approach. M.Sc. thesis, All India Institute of Medical Sciences (AIIMS), New Delhi, India, 2024. [Google Scholar] [Crossref]

21. S. Verma, Development of an AI-Based System for Kidney Stone Detection and Classification. Ph.D. dissertation, Indian Institute of Technology Delhi, New Delhi, India, 2025. [Google Scholar] [Crossref]

22. A. Mehta, Evaluation of Machine Learning Algorithms in Predicting Kidney Stone Composition. M.Sc. thesis, Banaras Hindu University, Varanasi, India, 2023. [Google Scholar] [Crossref]

23. U. Singh, P. K. Chaubey, K. Kant, and G. K. Srivastava, “Automated detection of chronic diseases from medical images using artificial intelligence,” International Journal of Applied Mathematics, vol. 38, no. 11S, 2025, doi: 10.12732/ijam.v38i11s.1316. [Google Scholar] [Crossref]

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