School of Automation, Banasthali Vidyapith, 304022, Rajasthan, India.
Applied AI Research Lab, Vermillion, SD, 57069, USA.
J Med Syst. 2023 Aug 23;47(1):91. doi: 10.1007/s10916-023-01983-8.
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.
不孕不育极大地扰乱了社会和婚姻生活，导致了紧张的情绪健康。早期诊断是最快适应这些变化的最需要的，这可以通过人工智能工具实现。我们的主要目标是理解人工智能在生育力检测中的作用，因为我们主要致力于寻找与不孕不育相关的生物标志物和相关风险因素。本文旨在生动地分析人工智能作为一种有效的筛查、预测不孕不育及相关风险因素的方法。我们使用三个科学数据库：PubMed、Web of Science 和 Scopus，通过技术术语收集相关文章：(人类不孕不育或人类生育力)和风险因素和(机器学习或人工智能或智能系统)。通过这种方式，我们系统地回顾了 42 篇文章并进行了荟萃分析。讨论了重要的发现和建议。其中包括数据增强、特征提取、可解释性的重要性，以及需要重新审视生育力分析有效系统的含义。此外，本文还概述了可以用来解决不孕不育及其相关风险因素的各种缓解措施。这些见解有助于更好地理解人工智能在生育力分析中的作用以及改善生殖健康结果的潜力。
