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AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review.

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Title: AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review.
Authors: GhoshRoy, Debasmita1,2, Alvi, P. A.3, Santosh, KC2,4 santosh.kc@usd.edu
Source: Journal of Medical Systems. 8/23/2023, Vol. 47 Issue 1, p1-21. 21p. 3 Diagrams, 14 Charts.
Subjects: Biomarkers, Online information services, Lifestyles, Obesity, Men's health, Meta-analysis, Systematic reviews, Age distribution, Artificial intelligence, Machine learning, Risk assessment, Infertility, Fertility, MEDLINE, Women's health, Reproductive health, Disease risk factors
Abstract: Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to comprehend the role of AI in fertility detection since we have primarily worked to find biomarkers and related risk factors associated with infertility. This paper aims to vividly analyse the role of AI as an effective method in screening, predicting for infertility and related risk factors. Three scientific repositories: PubMed, Web of Science, and Scopus, are used to gather relevant articles via technical terms: (human infertility OR human fertility) AND risk factors AND (machine learning OR artificial intelligence OR intelligent system). In this way, we systematically reviewed 42 articles and performed a meta-analysis. The significant findings and recommendations are discussed. These include the rising importance of data augmentation, feature extraction, explainability, and the need to revisit the meaning of an effective system for fertility analysis. Additionally, the paper outlines various mitigation actions that can be employed to tackle infertility and its related risk factors. These insights contribute to a better understanding of the role of AI in fertility analysis and the potential for improving reproductive health outcomes. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to comprehend the role of AI in fertility detection since we have primarily worked to find biomarkers and related risk factors associated with infertility. This paper aims to vividly analyse the role of AI as an effective method in screening, predicting for infertility and related risk factors. Three scientific repositories: PubMed, Web of Science, and Scopus, are used to gather relevant articles via technical terms: (human infertility OR human fertility) AND risk factors AND (machine learning OR artificial intelligence OR intelligent system). In this way, we systematically reviewed 42 articles and performed a meta-analysis. The significant findings and recommendations are discussed. These include the rising importance of data augmentation, feature extraction, explainability, and the need to revisit the meaning of an effective system for fertility analysis. Additionally, the paper outlines various mitigation actions that can be employed to tackle infertility and its related risk factors. These insights contribute to a better understanding of the role of AI in fertility analysis and the potential for improving reproductive health outcomes. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10916-023-01983-8
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        Text: English
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        PageCount: 21
        StartPage: 1
    Subjects:
      – SubjectFull: Biomarkers
        Type: general
      – SubjectFull: Online information services
        Type: general
      – SubjectFull: Lifestyles
        Type: general
      – SubjectFull: Obesity
        Type: general
      – SubjectFull: Men's health
        Type: general
      – SubjectFull: Meta-analysis
        Type: general
      – SubjectFull: Systematic reviews
        Type: general
      – SubjectFull: Age distribution
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Infertility
        Type: general
      – SubjectFull: Fertility
        Type: general
      – SubjectFull: MEDLINE
        Type: general
      – SubjectFull: Women's health
        Type: general
      – SubjectFull: Reproductive health
        Type: general
      – SubjectFull: Disease risk factors
        Type: general
    Titles:
      – TitleFull: AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review.
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              M: 08
              Text: 8/23/2023
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              Y: 2023
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