For the X400 magnification image dataset, the average precision (positive predictive value) of the algorithm was 82.6%, precision was 80.31%, and recall was 60.96%. We obtained a dataset of 7155 images at magnification X400 and a dataset of 9822 expansion images for the 5.0 X 5.0 cm cutouts. All images were uploaded to the Google Cloud AutoML vision platform. We defined four labels: Johnsen score 1-3, 4-5, 6-7, and 8-10 to distinguish Johnsen scores in clinical practice. In addition, we cut out of parts of the histopathology images (5.0 X 5.0 cm) for expansion of Johnsen’s characteristic areas with seminiferous tubules. We obtained testicular tissues for the 275 patients and were able to make 264 haematoxylin and eosin (H&E)-stained glass microscope slides. Average precision, precision, and recall assessed by the Google Cloud AutoML vision platform. We examined whether a tool for determining Johnsen scores automatically using artificial intelligence (AI) could be used in place of traditional Johnsen scoring to support pathologists’ evaluations.
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