Embryo selection, AI and reproductive choice DOI Creative Commons
Aurélie Halsband

AI and Ethics, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 20, 2024

Abstract In reproductive medicine, current research into the use of artificial intelligence (AI) to improve embryo selection has been met with enthusiasm. Within ethics, previous assessments AI-assisted have focused, for example, on liability gaps or risks arising from opaque decision-making. I argue that this focus ethical issues raised by AI in alone is incomplete because it neglects how AI’s convergence other innovative technologies raises further issues. describe acting as a catalyst social disruption human reproduction and profound change morality. The result improved culture, optimization through possibility selecting screened embryo. This technological interplay creates pull towards assisted reproduction, even those prospective parents who can reproduce without medical assistance. discussing fictional case parents, linked moral disruption. manifests itself deep uncertainty about legitimate ways procreating. explain rooted technology-induced concept choice. then outline debate should be reframed light

Language: Английский

Abnormal cleavage up to Day 3 does not compromise live birth and neonatal outcomes of embryos that have achieved full blastulation: a retrospective cohort study DOI Creative Commons
Tammy Lee, Kelli Peirce,

Jay Natalwala

et al.

Human Reproduction, Journal Year: 2024, Volume and Issue: 39(5), P. 955 - 962

Published: March 29, 2024

Abstract STUDY QUESTION Do embryos displaying abnormal cleavage (ABNCL) up to Day 3 have compromised live birth rates and neonatal outcomes if full blastulation has been achieved prior transfer? SUMMARY ANSWER ABNCL is associated with reduced but does not impact once achieved. WHAT IS KNOWN ALREADY? It widely accepted that implantation of when transferred at the stage. However, evidence scarce in literature reporting from blastocysts arising embryos, likely because they are ranked low priority for transfer. DESIGN, SIZE, DURATION This retrospective cohort study included 1562 consecutive autologous vitro fertilization cycles (maternal age 35.1 ± 4.7 years) performed Fertility North, Australia between January 2017 June 2022. Fresh transfers were on or 5, remaining cultured 6 before vitrification. A total 6019 subject blastocyst culture, a subset 664 resulting frozen was outcome analyses following single transfers. PARTICIPANTS/MATERIALS, SETTING, METHODS events annotated first mitotic division 3, including direct (DC), reverse (RC) <6 intercellular contact points 4-cell stage (<6ICCP). For DC RC combination, ratios affected blastomeres over number all also recorded. All pregnancies followed until gestational age, birthweight, sex baby being MAIN RESULTS AND THE ROLE OF CHANCE Full showing (19.5%), (41.7%), <6ICCP (58.8%), mixed (≥2) types (26.4%) lower than those without (67.2%, P < 0.01 respectively). Subgroup analysis showed declining increasing combined DC/RC 8-cell (66.2% 0 affected, 47.0% 0.25 27.4% 0.5 14.5% 0.75 7.7% 0.01). had achieved, no difference detected DC, RC, <6ICCP, subsequent (25.9%, 33.0%, 36.0% versus 30.8%, > 0.05, respectively), (38.7 1.6, 38.5 1.2, 38.3 3.5 1.8 weeks, respectively) birthweight (3343.0 649.1, 3378.2 538.4, 3352.6 841.3 3313.9 509.6 g, Multiple regression (logistic linear as appropriate) confirmed differences above measures after accounting potential confounders. LIMITATIONS, REASONS FOR CAUTION Our limited by its nature, making it impossible control every known unknown confounder. Embryos our dataset, surplus selection fresh transfer, may represent general embryo population. WIDER IMPLICATIONS FINDINGS findings highlight incremental ABNCL, depending ratio blastulation. The reassuring imply self-correction mechanism among reaching stage, which provides valuable guidance clinical practice patient counseling. FUNDING/COMPETTING INTEREST(S) research supported an Australian Government Research Training Program (RTP) Scholarship. authors report conflict interest. TRIAL REGISTRATION NUMBER N/A.

Language: Английский

Citations

7
Exploring the potential of machine learning in gynecological care: a review DOI
Imran Khan,

Brajesh Kumar Khare

Archives of Gynecology and Obstetrics, Journal Year: 2024, Volume and Issue: 309(6), P. 2347 - 2365

Published: April 16, 2024

Language: Английский

Citations

7
Balancing Technology, Ethics, and Society: A Review of Artificial Intelligence in Embryo Selection DOI Creative Commons
Roberto Aufieri,

Francesco Mastrocola

Information, Journal Year: 2025, Volume and Issue: 16(1), P. 18 - 18

Published: Jan. 2, 2025

The introduction of artificial intelligence (AI) in embryo selection during vitro fertilization presents distinct ethical and societal challenges compared to the general implementation AI healthcare. This narrative review examines perspectives potential implications implementing AI-driven selection. literature reveals that some authors perceive as an extension a technocratic paradigm commodifies embryos, considering any methods undermine dignity human life. Others, instead, contend prioritizing embryos with highest viability is morally permissible while cautioning against discarding based solely on unproven assessments. reviewed identified further concerns associated this technique, including possible bias criteria, lack transparency black-box algorithms, risks “machine paternalism” replacing judgment, privacy issues sensitive fertility data, equity access, maintaining human-centered care. These findings, along results only randomized controlled trial available, suggest clinical practice not currently scientifically ethically justified. Implementing deploying responsible would be feasible if raised are adequately addressed.

Language: Английский

Citations

0
Artificial Intelligence in Assisted Reproduction DOI
Michal Youngster,

Dvora Strassburger,

Irit Granot

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
The Evolving Scenarios of Artificial Intelligence in Assisted Reproductive Technologies DOI
Helena Machado, Susana Silva

Published: Jan. 1, 2025

Language: Английский

Citations

0
Machine Learning Applications in Male Factor Infertility: Have Cytokines a Role in the Diagnostic Work-Up of Varicocele? DOI
Nicole Dalia Cilia, Valerio Mario Salerno, Roberta Malaguarnera

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 279 - 292

Published: Jan. 1, 2025

Language: Английский

Citations

0
The Role of Artificial Intelligence in Female Infertility Diagnosis: An Update DOI Open Access
Necati Fındıklı,

Catherine Houba,

David Pening

et al.

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(9), P. 3127 - 3127

Published: April 30, 2025

Female infertility is a multifaceted condition affecting millions of women worldwide, with causes ranging from hormonal imbalances and genetic predispositions to lifestyle environmental factors. Traditional diagnostic approaches, such as assays, ultrasound imaging, testing, often require extensive time, resources, expert interpretation. In recent years, artificial intelligence (AI) has emerged transformative tool in the field reproductive medicine, offering advanced capabilities for improving accuracy, efficiency, personalization diagnosis treatment. AI technologies demonstrate significant potential analyzing vast complex datasets, identifying hidden patterns, providing data-driven insights that enhance clinical decision-making processes assisted (ART) services. This narrative review explores current advancements applications female diagnostics therapeutics, highlighting key technological innovations, their implications, existing limitations. It also discusses future revolutionizing healthcare. As AI-based continue evolve, integration into medicine expected pave way more accessible, cost-effective, personalized fertility care.

Language: Английский

Citations

0
Making and Selecting the Best Embryo in In vitro Fertilization DOI
R. Nuñez-Calonge, Nuria Santamaría, Teresa Rubio

et al.

Archives of Medical Research, Journal Year: 2024, Volume and Issue: 55(8), P. 103068 - 103068

Published: Aug. 26, 2024

Language: Английский

Citations

3
Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis DOI Creative Commons
Xin Xing,

Shanshan Wu,

He-Li Xu

et al.

EClinicalMedicine, Journal Year: 2024, Volume and Issue: 77, P. 102897 - 102897

Published: Oct. 24, 2024

Language: Английский

Citations

2
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