Automated spermatogenic staging in periodic acid-Schiff-stained testes of Sprague-Dawley rats using a deep learning...

The spermatogenic stage serves as a vital criterion for assessing normal spermatogenesis and is central to evaluating reproductive toxicity. Current manual methods for evaluating the spermatogenic stage are time-intensive, require expert knowledge, and are less effective at detecting subtle changes or comparing stage frequencies across samples. To overcome these limitations, this study introduces a method that leverages object detection models and Region-based Convolutional Neural Networks to enable efficient and accurate evaluation of spermatogenic stages. A total of 16 periodic acid-Schiff-stained testicular tissue whole-slide images (WSIs) obtained from 16 Sprague-Dawley rats were used in this study. A total of 14 stages were identified, and the approach was further applied to atrophied testicular samples as a real-world example. A total of 10 WSIs (nine normal and one atrophied testes) were used for model training, validation, and testing. Six additional WSIs (three normal and three atrophied testes) were used for model inference. For the test set, the model achieved a mean average precision of 0.869 and a mean average recall of 0.977 for detecting spermatogenic stages and atrophy. For the inference set, agreement with pathologist assessments exceeded 91%, providing objective benchmarks for stage evaluation and facilitating the comparison of stage frequencies across multiple samples. The model enabled the quantitative assessment of atrophied tissues by analyzing the proportional changes in atrophied seminiferous tubules relative to normal tubules. This automated approach has the potential to reduce the workload of pathologists by enabling rapid, reproducible assessment of toxicological changes during spermatogenesis. As a proof-of-concept, the integration of deep learning demonstrated the feasibility of improving the efficiency and objectivity of pathological evaluations in reproductive toxicity studies.