Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency DOI Creative Commons

Yaling Hew,

Duygu Kütük, Tuba Düzcü

et al.

Biology, Journal Year: 2024, Volume and Issue: 13(12), P. 988 - 988

Published: Nov. 28, 2024

Incorporating artificial intelligence (AI) into in vitro fertilization (IVF) laboratories signifies a significant advancement reproductive medicine. AI technologies, such as neural networks, deep learning, and machine promise to enhance quality control (QC) assurance (QA) through increased accuracy, consistency, operational efficiency. This comprehensive review examines the effects of on IVF laboratories, focusing its role automating processes embryo sperm selection, optimizing clinical outcomes, reducing human error. AI’s data analysis pattern recognition capabilities offer valuable predictive insights, enhancing personalized treatment plans increasing success rates fertility treatments. However, integrating also brings ethical, regulatory, societal challenges, including concerns about security, algorithmic bias, human–machine interface decision-making. Through an in-depth examination current case studies, advancements, future directions, this manuscript highlights how can revolutionize by standardizing processes, improving patient advancing precision It underscores necessity ongoing research ethical oversight ensure fair transparent applications sensitive field, assuring responsible use

Language: Английский

Current drawbacks and future perspectives in the diagnosis and treatment of male factor infertility, with a focus on FSH treatment: an expert opinion DOI Creative Commons
Daniele Santi, Giovanni Corona, Andrea Salonia

et al.

Journal of Endocrinological Investigation, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Infertility is defined as the inability to conceive after 1 year of unprotected intercourse, affecting approximately 15-20% couples in Western countries. It a shared problem within couple; when main issue lies with one partners, it preferable refer "male factor" or "female infertility rather than simply male female infertility. Despite factor accounting for half all couple cases, clinical approach partner not uniformly standardized across international guidelines. To provide an expert overview, we have comprehensively reviewed and critically analyzed most up-to-date literature on this sensitive topic, leading development proposal tailored assessment diagnostic-therapeutic pathway preventive strategies. The diagnostic also considers that infertile men are objectively less healthy their fertile counterparts same age ethnicity. This article discusses flow, classification infertility, definition idiopathic involvement general health, treatment recommendations, emphasizing follicle-stimulating hormone selected groups patients. We opinion current drawbacks future perspectives field, practical advice practice practitioners reproductive medicine.

Language: Английский

Citations

2

Reducing Quotation Errors in Scientific Manuscripts: A Novel Approach from the Global Andrology Forum DOI Creative Commons
Asli Metin Mahmutoglu, Ashok Agarwal, Bahadır Şahin

et al.

The World Journal of Men s Health, Journal Year: 2025, Volume and Issue: 43

Published: Jan. 1, 2025

This study investigated 1) the frequency of quotation errors in multi-authored medical manuscripts andrology, 2) analyzed common types and methods used to rectify them, 3) evaluated their impact on manuscript accuracy, credibility, research conclusions. Twelve written by Global Andrology Forum (GAF) members between 2023 2024 were randomly selected for this study. The "Quotation Verification Sheets" senior GAF researchers detect number errors. error rate was calculated total all cited references each manuscript. sections assessed using a 0-4 grading scale. Spearman correlation test assess scalar variables, Mann-Whitney U utilized compare variables two groups. median value 10.3%. Factual inaccuracy most type error, observed twelve at various rates. significantly associated with (ρ=0.706; p=0.010) in-text citations (ρ=0.636; p=0.026). (ρ=0.588; p=0.044) factual interpretation (ρ=0.861; p=0.013) also correlated However, no significant associations found author numbers or qualifications. adversely impacted discussion, followed overall message. Quotation are andrology-related scientific articles. Journal editorial offices should incorporate verification into review process. Limiting only strictly necessary ones may help improve accuracy. model proposed offers practical structured approach detecting correcting

Language: Английский

Citations

0

Factor Structure and Psychometric Properties of the Italian Version of the Childbearing Motivations Scale DOI Open Access
Angelica Gattamelata, Maria Elisabetta Coccia, Giulia Fioravanti

et al.

International Journal of Environmental Research and Public Health, Journal Year: 2025, Volume and Issue: 22(2), P. 186 - 186

Published: Jan. 28, 2025

The Childbearing Motivations Scale (CMS) is a multidimensional self-report measure of positive and negative motivations influencing the decision to become parent. This study aimed validate Italian version CMS. A sample 522 participants (27% men 73% women) aged from 18 55 years was recruited. four-factor model for subscale five-factor CMS demonstrated good fit. Reliability values ranged 0.70 0.91. Both factors had evidence convergent validity with sex, age, relationship duration: women reported lower in some mother contrast men. Moreover, greater becoming Those longer indicated motivations. No significant correlations were found subscale. Significant differences income levels (low vs. medium/high) regarding personal fulfillment, financial problems, body-image concerns, as well cultural (medium high) concerning economic constraints, intergenerational continuity, immaturity, physical suffering. These findings suggest that individuals resources scored higher across all these areas on Negative Our indicate can be used reliably assess parenthood among women.

Language: Английский

Citations

0

Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency DOI Creative Commons

Yaling Hew,

Duygu Kütük, Tuba Düzcü

et al.

Biology, Journal Year: 2024, Volume and Issue: 13(12), P. 988 - 988

Published: Nov. 28, 2024

Incorporating artificial intelligence (AI) into in vitro fertilization (IVF) laboratories signifies a significant advancement reproductive medicine. AI technologies, such as neural networks, deep learning, and machine promise to enhance quality control (QC) assurance (QA) through increased accuracy, consistency, operational efficiency. This comprehensive review examines the effects of on IVF laboratories, focusing its role automating processes embryo sperm selection, optimizing clinical outcomes, reducing human error. AI’s data analysis pattern recognition capabilities offer valuable predictive insights, enhancing personalized treatment plans increasing success rates fertility treatments. However, integrating also brings ethical, regulatory, societal challenges, including concerns about security, algorithmic bias, human–machine interface decision-making. Through an in-depth examination current case studies, advancements, future directions, this manuscript highlights how can revolutionize by standardizing processes, improving patient advancing precision It underscores necessity ongoing research ethical oversight ensure fair transparent applications sensitive field, assuring responsible use

Language: Английский

Citations

2