OPEN ACCESS | Published on : 02-Mar-2026 | Pages: 74-78 | Doi : 10.37446/edibook202024/74-78
Assisted reproductive technologies play a major role in animal reproduction and conservation. Accuracy in semen analysis is an indispensable component for the success of Assisted Reproductive Technologies, as male infertility may contribute up to 40 to 50 % of pregnancy failure cases. The manual analysis of semen may be subjective, time-consuming, and error-prone. It was improved by the introduction of a superior technique, the Computer-Aided Sperm Analysis, which enhanced the examination of viability and kinematic parameters of spermatozoa. Recently, artificial intelligence, especially integrated with deep convolutional neural networks (DCNN), has depicted immense potential in enhancing the efficiency of semen analysis. Such systems efficiently and speedily perform the objective analysis of sperm morphology and motility through image recognition as they are trained with massive annotated datasets through supervised learning.
Assisted Reproductive Technologies, Artificial Intelligence, Image Recognition, Semen Analysis, CASA
Agarwal, A., Mulgund, A., Hamada, A., & Chyatte, M. R. (2015). A unique view on male infertility around the globe. Reproductive Biology and Endocrinology, 13, Article 37.
Andersen, J. M., Witczak, O., Due, E. U., Hicks, S., Thambawita, V., Björndahl, L., Riegler, M., & Haugen, T. B. (2024). Sperm motility assessed by deep convolutional neural networks. Reproductive BioMedicine Online, 48(1), 104060.
Baker, S., & Xiang, W. (2023). Artificial intelligence of things for smarter healthcare: A survey of advancements, challenges, and opportunities. IEEE Communications Surveys & Tutorials, 25, 1261–1293.
Baldán, F. J., García-Gil, D., & Fernandez-Basso, C. (2025). Revolutionizing sperm analysis with AI: A review of computer-aided sperm analysis systems. Computation, 13, 132.
Barroso, G., Mercan, R., Ozgur, K., Morshedi, M., Kolm, P., Coetzee, K., Oehninger, S. (1999). Intra- and inter-laboratory variability in the assessment of sperm morphology by strict criteria: Impact of semen preparation, staining techniques and manual versus computerized analysis. Human Reproduction, 14(8), 2036–2040.
Bormann, C. L., Thirumalaraju, P., Kanakasabapathy, M. K., Kandula, H., Souter, I., Dimitriadis, I., Shafiee, H. (2020). Consistency and objectivity of automated embryo assessments using deep neural networks. Fertility and Sterility, 113(4), 781–787.e1.
Butola, A., Popova, D., Prasad, D. K., Ahmad, A., Habib, A., Tinguely, J. C., Mehta, S. K. (2020). High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition. Scientific Reports, 10, 13118.
Ciani, F., Cocchia, N., Esposito, L., & Avallone, L. (2012). Fertility cryopreservation. In B. Wu (Ed.), Advances in Embryo Transfer (pp. 225–248). IntechOpen.
Ciani, F., Cocchia, N., Rizzo, M., Ponzio, P., Tortora, G., Avallone, L., Lorizio, R. (2008). Sex determining of cat embryo and some feline species. Zygote, 16(2), 169–177.
Douglas, C., Parekh, N., Kahn, L. G., Henkel, R., & Agarwal, A. (2021). A novel approach to improving the reliability of manual semen analysis: A paradigm shift in the workup of infertile men. World Journal of Men’s Health, 39(2), 172–185.
Fauser, B. C. (2019). Towards the global coverage of a unified registry of IVF outcomes. Reproductive BioMedicine Online, 38(2), 133–137.
Finelli, R., Leisegang, K., Tumallapalli, S., Henkel, R., & Agarwal, A. (2021). The validity and reliability of computer-aided semen analyzers in performing semen analysis: A systematic review. Translational Andrology and Urology, 10(7), 3069.
Hirsh, A. (2003). Male subfertility. BMJ, 327, 669–672.
Hong, S., Kwak, S., & Han, B. (2017). Weakly supervised learning with deep convolutional neural networks for semantic segmentation: Understanding semantic layout of images with minimum human supervision. IEEE Signal Processing Magazine, 34(6), 39–49.
Kakkar, P., Gupta, S., Paschopoulou, K. I., Paschopoulos, I., Paschopoulos, I., Siafaka, V., & Tsonis, O. (2025). The integration of artificial intelligence in assisted reproduction: A comprehensive review. Frontiers in Reproductive Health, 7, Article 1520919.
Kandel, M. E., Rubessa, M., He, Y. R., Schreiber, S., Meyers, S., Matter Naves, L., Popescu, G. (2020). Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure. Proceedings of the National Academy of Sciences, 117(31), 18302–18309.
Mienye, I. D., Swart, T. G., Obaido, G., Jordan, M., & Ilono, P. (2024). Deep convolutional neural networks: A comprehensive review. Preprints.
Oseguera-Lopez, I., Ruiz-Díaz, S., Ramos-Ibeas, P., & Pérez-Cerezales, S. (2019). Novel techniques of sperm selection for improving IVF and ICSI outcomes. Frontiers in Cell and Developmental Biology, 7, 298.
Sethi, M., Shah, N., Kumar, P., Gupta, V., Rohilla, A., Bhatia, N., Kumar, N., Bhakat, M., & Mohanty, T. K. (2021). CASA (computer-assisted semen analysis): As a tool for analysis of bull fertility. Indian Farmer, 8(04), 293–299.
Stockman, G., & Shapiro, L. G. (2001). Computer vision. Prentice Hall.
