High-throughput sperm DNA analysis at the single-cell and population levels

Simchi, Mohammad; Riordon, Jason; Wang, Yihe; McCallum, Christopher; You, Jae Bem; Jarvi, Keith; Nosrati, Reza; Sinton, David

doi:10.1039/d3an00564j

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2024

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Cited by 2 publications

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“…Recently, models for automation of the SCD test have been introduced, 39–41 but most of them are semi-quantitative classifier models. We establish our modified standard upon the commonly used definition of the relative halo radius and take into consideration the variable staining status and inter-operator variability.…”

Section: Resultsmentioning

confidence: 99%

Gaussian clustering and quantification of the sperm chromatin dispersion test using convolutional neural networks

Yang,

Zhang,

Fan

et al. 2024

Analyst

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Section: Resultsmentioning

confidence: 99%

Gaussian clustering and quantification of the sperm chromatin dispersion test using convolutional neural networks

Yang,

Zhang,

Fan

et al. 2024

Analyst

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Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning

Shahali,

Murshed,

Spencer

et al. 2024

Advanced Intelligent Systems

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Sperm morphology analysis is crucial in infertility diagnosis and treatment. However, current clinical analytical methods use either chemical stains that render cells unusable for treatment or rely on subjective manual inspection. Here, an ensemble deep‐learning model is presented for classification of live, unstained human sperm using whole‐cell morphology. This model achieves an accuracy and precision of 94% benchmarked against the consensus of three andrology scientists who classified the images independently. The model loses less than a 12% prediction performance even when image resolution is reduced by over sixfold. This ensures compatibility across varied clinical imaging setups. This model also provides a high certainty and robust classification of challenging images, which divided the experts. By providing a consistent, automated approach for classifying live, unstained cells using quantitative data, this model offers promising future opportunities for enhancing clinical sperm selection practices and reducing day‐to‐day variability in clinics.

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High-throughput sperm DNA analysis at the single-cell and population levels

Abstract: An automated analysis method for the sperm chromatin dispersion assay is demonstrated that quantifies DNA quality of more than 1000 of sperm at the single-cell and population levels within 7 minutes – a 40-fold reduction in processing time.

Cited by 2 publications

References 50 publications

Gaussian clustering and quantification of the sperm chromatin dispersion test using convolutional neural networks

Gaussian clustering and quantification of the sperm chromatin dispersion test using convolutional neural networks

Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning

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