Too good to be true? AI performance in CT-based urinary stone detection and classification: EAU endourology systematic review and diagnostic accuracy meta-analysis

Ali Talyshinskii, Lazaros Tzelves, Vineet Gauhar, Carlotta Nedbal, Ivan Gorgotsky, Nariman Gadzhiev, Vigen Malkhasyan, Danielle Castellani, Selçuk Güven, Karl Kowalewski, Thomas Knoll, Tzevat Tefik, Bhaskar Kumar Somani

Open source

DOI
10.1007/s00345-026-06885-1
Published
2026-11-05
Container
World Journal of Urology
Publisher
Springer Science and Business Media LLC
Open access
unknown

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BibTeX

@article{allodium:10.1007/s00345-026-06885-1,
  title = {Too good to be true? AI performance in CT-based urinary stone detection and classification: EAU endourology systematic review and diagnostic accuracy meta-analysis},
  author = {Ali Talyshinskii and Lazaros Tzelves and Vineet Gauhar and Carlotta Nedbal and Ivan Gorgotsky and Nariman Gadzhiev and Vigen Malkhasyan and Danielle Castellani and Selçuk Güven and Karl Kowalewski and Thomas Knoll and Tzevat Tefik and Bhaskar Kumar Somani},
  year = {2026},
  journal = {World Journal of Urology},
  doi = {10.1007/s00345-026-06885-1},
  url = {https://doi.org/10.1007/s00345-026-06885-1}
}

RIS

TY  - JOUR
TI  - Too good to be true? AI performance in CT-based urinary stone detection and classification: EAU endourology systematic review and diagnostic accuracy meta-analysis
AU  - Ali Talyshinskii
AU  - Lazaros Tzelves
AU  - Vineet Gauhar
AU  - Carlotta Nedbal
AU  - Ivan Gorgotsky
AU  - Nariman Gadzhiev
AU  - Vigen Malkhasyan
AU  - Danielle Castellani
AU  - Selçuk Güven
AU  - Karl Kowalewski
AU  - Thomas Knoll
AU  - Tzevat Tefik
AU  - Bhaskar Kumar Somani
PY  - 2026
JO  - World Journal of Urology
DO  - 10.1007/s00345-026-06885-1
UR  - https://doi.org/10.1007/s00345-026-06885-1
ER  - 

APA

Talyshinskii, A., Tzelves, L., Gauhar, V., Nedbal, C., Gorgotsky, I., Gadzhiev, N., Malkhasyan, V., Castellani, D., Güven, S., Kowalewski, K., Knoll, T., Tefik, T., & Somani, B. K. (2026). Too good to be true? AI performance in CT-based urinary stone detection and classification: EAU endourology systematic review and diagnostic accuracy meta-analysis. World Journal of Urology. https://doi.org/10.1007/s00345-026-06885-1

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