ArticleScientific reports2021
A method for utilizing automated machine learning for histopathological classification of testis based on Johnsen scores.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06765512 (Artificial Intelligence-Based Machine Learning to Classify Adenomyosis from Ultrasound Scans), which is not on this map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Artificial Intelligence-Based Machine Learning to Classify Adenomyosis from Ultrasound Scans: a Multicentre Model Development Study
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Automated Bergmann-Kliesch Score assessment in testicular biopsies using artificial intelligence-based whole-slide image analysis: a retrospective monocentric development and validation study.Basic and clinical andrology · 2026Article
- Context-dependent effects of whole-body vibration on reproductive function and penile tissue integrity in sedentary male rats.Experimental physiology · 2026Article
- Single Intraperitoneal Busulfan Injection Induces Long-Term Reproductive Dysfunction and Reduces Male Offspring Ratio in BALB/c Mice.Journal of applied toxicology : JAT · 2026Article
- Ganoderic acid D as a potential modulator of steroidogenic enzymes: Protective effects of Cucurbita maxima Duch. seed extract against nonylphenol-induced reproductive toxicity.Journal of computer-aided molecular design · 2026Article
- Nighttime-restricted feeding disrupts spermatogenesis in mice via gut microbiota-derived KYNA homeostasis.Communications biology · 2026Article
- Dose-Dependent Effects of Intratesticular Adipose-Derived Mesenchymal Stem Cell Injection on Heat-Induced Spermatogenesis Disorder in Wistar Rats: Focus on Apoptosis and Oxidative Stress.Iranian journal of pathology · 2026Article
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- Rapid detection of mouse spermatogenic defects by testicular cellular composition analysis via enhanced deep learning model.Andrology · 2025Article
- Technologies to improve sperm retrieval in men undergoing micro-TESE for NOA.Asian journal of andrology · 2025Review
- Unseen Threats: The Long-term Impact of PET-Microplastics on Development of Male Reproductive Over a Lifetime.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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- Circular RNAs: novel noncoding players in male infertility.Hereditas · 2024Review
- Assessment of reproductive, genotoxic, and cytotoxic effects of leachate-contaminated water in male mice.Heliyon · 2024Article
- Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature.Healthcare (Basel, Switzerland) · 2024Review
- Large language models streamline automated machine learning for clinical studies.Nature communications · 2024Article
- Application of automated machine learning for histological evaluation of feline endoscopic samples.The Journal of veterinary medical science · 2024Article
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- Rapid azoospermia classification by stimulated Raman scattering and second harmonic generation microscopy.Biomedical optics express · 2023Article
- Male infertility.Nature reviews. Disease primers · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. Average precision, precision, and recall were assessed by the Google Cloud AutoML Vision platform. We obtained testicular tissues for 275 patients and were able to use haematoxylin and eosin (H&E)-stained glass microscope slides from 264 patients. In addition, we cut out of parts of the histopathology images (5.0 × 5.0 cm) for expansion of Johnsen's characteristic areas with seminiferous tubules. We defined four labels: Johnsen score 1-3, 4-5, 6-7, and 8-10 to distinguish Johnsen scores in clinical practice. All images were uploaded to the Google Cloud AutoML Vision platform. We obtained a dataset of 7155 images at magnification 400× and a dataset of 9822 expansion images for the 5.0 × 5.0 cm cutouts. For the 400× magnification image dataset, the average precision (positive predictive value) of the algorithm was 82.6%, precision was 80.31%, and recall was 60.96%. For the expansion image dataset (5.0 × 5.0 cm), the average precision was 99.5%, precision was 96.29%, and recall was 96.23%. This is the first report of an AI-based algorithm for predicting Johnsen scores.
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