Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning

Author:

Shahali Sahar1ORCID,Murshed Mubasshir2,Spencer Lindsay1,Tunc Ozlem3,Pisarevski Ludmila3,Conceicao Jason3,McLachlan Robert34,O’Bryan Moira K.5,Ackermann Klaus6,Zander‐Fox Deirdre378,Neild Adrian1,Nosrati Reza1ORCID

Affiliation:

1. Department of Mechanical and Aerospace Engineering Monash University Clayton Victoria 3800 Australia

2. Monash Deep Neuron Monash University Clayton Victoria 3800 Australia

3. Monash IVF Group Cremorne Victoria 3121 Australia

4. Clinical Andrology Hudson Institute of Medical Research Monash University Clayton 3168 Australia

5. School of BioSciences and Bio21 Institute University of Melbourne Parkville Victoria 3010 Australia

6. SoDa Labs and Department of Econometrics and Business Statistics Monash Business School Clayton Victoria 3800 Australia

7. Biomedical Discovery Institute Monash University Clayton 3168 Australia

8. Department of Biomedicine University of Adelaide Adelaide 5000 Australia

Abstract

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.

Funder

Australian Research Council

National Health and Medical Research Council

Publisher

Wiley

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