AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review.
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 s...
| Publicado en: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 22 |
|---|---|
| Autores principales: | , , |
| Formato: | algorithm pictorial research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
8/23/2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=170716538&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 170716538 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 8/23/2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 170716538 170716538 170716538 10.1007/s10916-023-01983-8 170716538 ppf: 1 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review. aug: au: GhoshRoy, Debasmita Alvi, P. A. Santosh, KC affil: School of Automation, Banasthali Vidyapith, 304022, Rajasthan, India sug: subj: Artificial Intelligence Fertility Evaluation Clinical Assessment Tools Risk Assessment Infertility Diagnosis Biological Markers Infertility Risk Factors Men's Health Women's Health Human Systematic Review Meta Analysis PubMed Machine Learning Life Style Age Factors Obesity Reproductive Health ab: 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. pubtype: Academic Journal doctype: algorithm meta analysis pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|