This study aimed to evaluate the approaches of pediatric surgeons and pediatric urologists in Türkiye regarding the diagnosis and treatment of testicular torsion (TT) and their adherence to the European Association of Urology (EAU) pediatric urology guideline. A survey consisting of 19 questions, accompanied by an annotation describing the objective of the study, was emailed to pediatric surgeons and pediatric urologists in June and July 2023. Of the 95 respondents, 62.1% had over 10 years of experience, and 48.4% treated more than five cases of TT annually. Of the participants, 87.4% stated that scrotal Doppler ultrasonography (US) was always used, and 12.6% stated that US was used in cases of doubtful diagnosis. Concerning treatment, 14.7% reported performing manual detorsion, 70.5% never did, and 14.7% did so only if the operating room was unavailable soon. A total of 92.6% of participants opted for emergency surgery. Among participants who perform manual detorsion, 71.4% perform surgery within 24 hours after successful manual detorsion. Regarding fixation of the contralateral testicle, 14.7% never performed it, and 27.4% did so only when they performed an orchiectomy on the torsion testicle. While most participants follow EAU pediatric urology guidelines by performing emergency surgery, the rate of manual detorsion is low. Few participants stated that emergency surgery may not be performed after manual detorsion. While all of the participants performed fixation of the torsion testicle in accordance with the guidelines, the same adherence was not observed in the contralateral testicle.

Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, with Escherichia coli and Klebsiella pneumoniae being the predominant pathogens. Diagnostic delays necessitate the use of broad-spectrum antibiotics, which fuels antimicrobial resistance. This study aimed to develop and validate an artificial intelligence (AI) model for the rapid identification of E. coli and K. pneumoniae colonies from urine culture images. We analyzed 1547 chromogenic agar urine culture images (850 E. coli, 697 K. pneumoniae) obtained using an automated Copan WASP system. A YOLOv12 deep learning model was trained on expert-labeled colonies. The reference standard comprised MALDI-TOF Mass Spectrometry (MS) for K. pneumoniae and a characteristic chromogenic morphology for E. coli. This phenotypic method is standard in clinical practice, with validation studies for the specific agar used reporting a sensitivity of 98.1–99.0% and a specificity of 99.1% for E. coli identification. Performance was assessed on an internal test set and via external validation using 91 independent images. The model achieved 99% accuracy (precision, recall, F1-score: 0.99) on internal testing. External validation yielded 100% accuracy, with the critical note that species labels in this independent set were inferred from colony colour. Rare errors involved atypical (eg, gold-pigmented) E. coli colonies. YOLOv12 outperformed five benchmark deep learning models. This AI model enables rapid (sub-second), accurate phenotypic classification of the most common UTI pathogens directly from routine culture plates. Integration into automated systems could reduce the diagnostic timeline by approximately 18 hours, facilitating earlier targeted therapy and supporting antimicrobial stewardship. A key consideration for implementation is the model’s basis in phenotypic identification, which aligns with standard laboratory workflow but requires awareness of rare chromogenic variants. The reliance on chromogenic morphology for E. coli ground truth has been a key limitation of the study.