Yanase, T.; Unno, R.; Tokas, T.; Gauhar, V.; Sasaki, Y.; Kawase, K.; Chaya, R.; Hamamoto, S.; Maruyama, M.; Yasui, T.;
et al. AI-Driven Prediction of Renal Stone Recurrence Following ECIRS: A Machine Learning Approach to Postoperative Risk Stratification Incorporating 24-Hour Urine Data. J. Clin. Med. 2025, 14, 4037.
https://doi.org/10.3390/jcm14124037
AMA Style
Yanase T, Unno R, Tokas T, Gauhar V, Sasaki Y, Kawase K, Chaya R, Hamamoto S, Maruyama M, Yasui T,
et al. AI-Driven Prediction of Renal Stone Recurrence Following ECIRS: A Machine Learning Approach to Postoperative Risk Stratification Incorporating 24-Hour Urine Data. Journal of Clinical Medicine. 2025; 14(12):4037.
https://doi.org/10.3390/jcm14124037
Chicago/Turabian Style
Yanase, Takahiro, Rei Unno, Theodoros Tokas, Vineet Gauhar, Yuya Sasaki, Kengo Kawase, Ryosuke Chaya, Shuzo Hamamoto, Mihoko Maruyama, Takahiro Yasui,
and et al. 2025. "AI-Driven Prediction of Renal Stone Recurrence Following ECIRS: A Machine Learning Approach to Postoperative Risk Stratification Incorporating 24-Hour Urine Data" Journal of Clinical Medicine 14, no. 12: 4037.
https://doi.org/10.3390/jcm14124037
APA Style
Yanase, T., Unno, R., Tokas, T., Gauhar, V., Sasaki, Y., Kawase, K., Chaya, R., Hamamoto, S., Maruyama, M., Yasui, T., & Taguchi, K.
(2025). AI-Driven Prediction of Renal Stone Recurrence Following ECIRS: A Machine Learning Approach to Postoperative Risk Stratification Incorporating 24-Hour Urine Data. Journal of Clinical Medicine, 14(12), 4037.
https://doi.org/10.3390/jcm14124037