ArticleAsian journal of andrology2024
Machine learning approach to assess the association between anthropometric, metabolic, and nutritional status and semen parameters.
Article in Asian journal of andrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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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.
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Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- Integrated Anthropometric, Physiological and Biological Assessment of Elite Youth Football Players Using Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Reassessing Semen Analysis: Clinical Insights Beyond Sperm Count and Motility.Reproductive medicine and biologyReview
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many lifestyle factors, such as nutritional imbalance leading to obesity, metabolic disorders, and nutritional deficiency, have been identified as potential risk factors for male infertility. The aim of this study was to evaluate the relationship between semen parameters and anthropometric, metabolic and nutritional parameters. Relationship was first assessed individually, then after the application of a previously constructed and validated machine learning score that allows their combination. Anthropometric, metabolic, antioxidant, micronutrient, and sperm parameters from 75 men suffering from idiopathic infertility from four infertility centers in France (Jean-Verdier ART Center Hospital, Bondy; North Hospital ART Center, Saint-Étienne; Navarre Polyclinic ART Center, Pau; and Cochin Hospital ART Center, Paris) between September 2009 and December 2013 were collected. After assessing standard correlation analysis, a previously built machine learning model, providing a score ranging from 0 (the poorest) to 1 (the most favorable), was calculated for each man in the study cohort. This machine learning model, which separates infertile/fertile men with unexplained infertility on the basis of their bioclinical signature, provides a more holistic evaluation of the influence of the considered markers (anthropometric, metabolic, and oxidative status). We observed a significant correlation of some anthropometric, metabolic, and nutritional disorders with some sperm characteristics. Moreover, an unfavorable machine learning score was associated with a high level of sperm DNA fragmentation. Favorable anthropometric, metabolic, and oxidative patterns, which may reflect an appropriate lifestyle, appear to positively impact overall health, in particular reproductive function. This study, consistent with previous publications, suggests that beyond semen quality parameters, in an essential assessment of male fertility, other key factors should be taken into account. In this regard, the application of emerging artificial intelligence techniques may provide a unique opportunity to integrate all these parameters and deliver personalized care.
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Registered trials
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