Automated Bergmann-Kliesch Score assessment in testicular biopsies using artificial intelligence-based whole-slide image analysis: a retrospective monocentric development and validation study

Abstract Background The Bergmann-Kliesch Score is a histopathological grading system used to evaluate spermatogenesis in testicular biopsies, quantifying the fraction of seminiferous tubules containing elongated spermatids. It serves as a key parameter in the clinical process of men with non-obstructive azoospermia (NOA) undergoing testicular sperm extraction (TESE) for subsequent intracytoplasmic sperm injection (ICSI). Manual Bergmann-Kliesch Score assessment is time-consuming and subject to inter- and intraobserver variability. This study aimed to develop and validate an artificial intelligence (AI)-based computational pipeline for automated Bergmann-Kliesch Score assessment from digitized testicular biopsy whole-slide images (WSIs). Results A retrospective monocentric cohort of 74 patients who underwent TESE at the University Medical Center Frankfurt was analyzed. A dual-model machine-learning pipeline was used to process paired haematoxylin-eosin (HE) and OCT3/4 immunohistochemically stained WSIs. A U-Net architecture with a ResNet-34 backbone was trained on 60 HE-stained slides for tubular segmentation, monitored by a multi-class Dice coefficient of 0.897 on the validation set. A second U-Net model with a ResNet-34 backbone was trained on 15 OCT3/4-stained slides comprising 14,226 manually annotated elongated spermatids for spermatid detection. Both modalities were integrated via multimodal image registration combining rigid alignment and B-spline warping, enabling spatial projection of spermatid detections onto tubular segmentations. On the patient-level held-out test set of 15 patients, AI-derived Bergmann-Kliesch Scores showed strong agreement with pathologist-assigned reference scores (Pearson r = 0.957, Spearman ρ = 0.856, R 2 = 0.916, mean absolute error = 0.93, root mean squared error = 1.28). At the clinical cutoff of Bergmann-Kliesch Score ≥ 1, the pipeline correctly classified all test-set cases. Across the full evaluation cohort of 74 patients, agreement remained robust (Pearson r = 0.851, Spearman ρ = 0.829, mean absolute error = 1.52, root mean squared error = 2.20). Conclusions This AI-based pipeline enables automated, reproducible Bergmann-Kliesch Score assessment from routinely stained testicular biopsy WSIs, demonstrating strong agreement with manual pathologist evaluation. The system shows promise as a clinical decision-support tool in TESE workflows. External multicentric validation and prospective correlation with ICSI outcome data are warranted in future studies.