Artificial intelligence across the stone-associated urinary infection pathway: imaging, urosepsis prediction, and antimicrobial stewardship
- 1Department of Urology, Hospital Universitario Barrios Unidos
- 2Department of Urology, Clinica Universitaria Medicina Integral - CUMI
Abstract
Complicated urinary tract infection associated with urinary stone disease remains clinically challenging because obstruction, infected stones, delayed microbiological confirmation, and antimicrobial resistance may converge in the same patient. Artificial intelligence can support this pathway when it is framed as clinical risk integration rather than isolated image recognition. We conducted a structured narrative search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar from database inception to 31 July 2026, supplemented by reference chaining and relevant clinical and reporting guidelines. The synthesis distinguishes direct evidence in stone-associated infection and perioperative infectious outcomes from indirect evidence in stone detection, volumetry, recurrence, and general UTI stewardship. Radiomics and machine learning studies show promising discrimination, but the evidence remains predominantly retrospective and frequently lacks external validation, complete calibration reporting, and prospective impact assessment. Artificial intelligence should therefore be treated as an adjunct to urological assessment, cultures, imaging, antimicrobial stewardship, and timely source control. For African settings, pragmatic validation must account for local resistance patterns, uneven access to CT and microbiology, referral delays, infrastructure, and the availability of urgent drainage.
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BibTeX
@article{ordonezzabala2026artificial,
title = {{Artificial intelligence across the stone-associated urinary infection pathway: imaging, urosepsis prediction, and antimicrobial stewardship}},
author = {Andrea Carolina Ordonez-Zabala and Andrea Vanessa Rey-Contreras and Maria Alejandra Arrazola-Villadiego},
journal = {African Journal of Urology},
year = {2026},
month = oct,
doi = {10.1186/s12301-026-00623-0},
url = {https://doi.org/10.1186/s12301-026-00623-0},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence across the stone-associated urinary infection pathway: imaging, urosepsis prediction, and antimicrobial stewardship
AU - Andrea Carolina Ordonez-Zabala
AU - Andrea Vanessa Rey-Contreras
AU - Maria Alejandra Arrazola-Villadiego
PY - 2026
DA - 2026/10/25
JO - African Journal of Urology
DO - 10.1186/s12301-026-00623-0
UR - https://doi.org/10.1186/s12301-026-00623-0
AB - Complicated urinary tract infection associated with urinary stone disease remains clinically challenging because obstruction, infected stones, delayed microbiological confirmation, and antimicrobial resistance may converge in the same patient. Artificial intelligence can support this pathway when it is framed as clinical risk integration rather than isolated image recognition. We conducted a structured narrative search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar from database inception to 31 July 2026, supplemented by reference chaining and relevant clinical and reporting guidelines. The synthesis distinguishes direct evidence in stone-associated infection and perioperative infectious outcomes from indirect evidence in stone detection, volumetry, recurrence, and general UTI stewardship. Radiomics and machine learning studies show promising discrimination, but the evidence remains predominantly retrospective and frequently lacks external validation, complete calibration reporting, and prospective impact assessment. Artificial intelligence should therefore be treated as an adjunct to urological assessment, cultures, imaging, antimicrobial stewardship, and timely source control. For African settings, pragmatic validation must account for local resistance patterns, uneven access to CT and microbiology, referral delays, infrastructure, and the availability of urgent drainage.
SN - 1961-9987
SN - 1110-5704
ER -