Intraoperative indocyanine green fluorescence imaging and multimodal deep learning for predicting non-salvage outcomes in pediatric testicular torsion

Introduction Intraoperative assessment of testicular viability in pediatric torsion remains subjective. We evaluated a multimodal indocyanine green near-infrared fluorescence (ICG-NIRF) deep learning model for predicting a composite non-salvage outcome. Methods This single-center retrospective analysis used prospectively collected images from consecutive boys with surgically confirmed torsion who underwent emergency exploration and ICG-NIRF imaging. Image collection began in March 2022, and model development was undertaken in January 2025. The endpoint was orchiectomy or ultrasound-confirmed atrophy after orchiopexy, defined as an affected-to-contralateral testicular volume ratio <50% at 6 months. Patients were divided into development ( n = 107) and internal evaluation ( n = 46) cohorts. A traditional logistic model was compared descriptively with a two-stream model using operative photographs and standardized 30-s fluorescence images. Results Among 153 patients, 52 (34.0%) reached the endpoint. In internal evaluation, the traditional model achieved an area under the receiver operating characteristic curve of 0.889 and a Brier score of 0.182. The multimodal model achieved an area under the curve of 0.946 and a Brier score of 0.128. Conclusion Multimodal ICG-NIRF deep learning may provide an objective adjunct for intraoperative decision-making in pediatric testicular torsion. External validation is required.