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Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction

Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data are not used to its fullest potential. Manual evaluation of a semen sample using...

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Autores principales: Hicks, Steven A., Andersen, Jorunn M., Witczak, Oliwia, Thambawita, Vajira, Halvorsen, Pål, Hammer, Hugo L., Haugen, Trine B., Riegler, Michael A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6856178/
https://www.ncbi.nlm.nih.gov/pubmed/31727961
http://dx.doi.org/10.1038/s41598-019-53217-y
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author Hicks, Steven A.
Andersen, Jorunn M.
Witczak, Oliwia
Thambawita, Vajira
Halvorsen, Pål
Hammer, Hugo L.
Haugen, Trine B.
Riegler, Michael A.
author_facet Hicks, Steven A.
Andersen, Jorunn M.
Witczak, Oliwia
Thambawita, Vajira
Halvorsen, Pål
Hammer, Hugo L.
Haugen, Trine B.
Riegler, Michael A.
author_sort Hicks, Steven A.
collection PubMed
description Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data are not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology. We use modern and classical machine learning techniques together with a dataset consisting of 85 videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. Adding participant data did not improve the algorithms performance. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research.
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spelling pubmed-68561782019-12-17 Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction Hicks, Steven A. Andersen, Jorunn M. Witczak, Oliwia Thambawita, Vajira Halvorsen, Pål Hammer, Hugo L. Haugen, Trine B. Riegler, Michael A. Sci Rep Article Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data are not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology. We use modern and classical machine learning techniques together with a dataset consisting of 85 videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. Adding participant data did not improve the algorithms performance. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research. Nature Publishing Group UK 2019-11-14 /pmc/articles/PMC6856178/ /pubmed/31727961 http://dx.doi.org/10.1038/s41598-019-53217-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Hicks, Steven A.
Andersen, Jorunn M.
Witczak, Oliwia
Thambawita, Vajira
Halvorsen, Pål
Hammer, Hugo L.
Haugen, Trine B.
Riegler, Michael A.
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
title Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
title_full Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
title_fullStr Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
title_full_unstemmed Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
title_short Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
title_sort machine learning-based analysis of sperm videos and participant data for male fertility prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6856178/
https://www.ncbi.nlm.nih.gov/pubmed/31727961
http://dx.doi.org/10.1038/s41598-019-53217-y
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