Investigating the Artificial intelligence in prediction and evaluation sperm and embryo quality in In vitro fertilization (IVF): A systematic review DOI Creative Commons

shahrzad kaveh,

Aida Ghafari,

zahra khedri

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 25, 2024

Abstract Importance: Assisted Reproductive Technologies (ART) have been developed to address infertility by improving embryo selection. Artificial intelligence (AI), using Time-Lapse Imaging (TLI), enhances predictions from fertilization the blastocyst stage. Objective: Studies show AI can identify suitable embryos more effectively than specialists, in-vitro (IVF) success rates enhancing transfer and reducing miscarriage risks. With IVF below 40%, it is essential explore methods boost outcomes. Findings: A systematic review in October 2024 searched databases like PubMed Scopus terms related AI, excluding non-English qualitative studies. Twenty-seven studies were reviewed; 17 predicted treatment responses with deep learning. Two used neural networks for successful prediction, eight employed ML such as NB, SVM, RF, an average AUC of 0.91. Models showed 90-96% accuracy, sensitivity, precision. Conclusion: technologies, particularly NB Reinforcement Learning, promise outcomes classification diagnosis while saving time. Interdisciplinary approaches micro Nano-biotechnology help overcome clinical challenges. Relevance: Examining quality sperm egg separately could further improve fertility testing ART, optimizing results.

Language: Английский

A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems DOI Creative Commons

Guillaume Canat,

A Duval,

Nina Gidel-Dissler

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 22, 2024

The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms assist embryologists their morphokinetic evaluation. Today, most literature characterized that predict pregnancy, ploidy or blastocyst quality, leaving side task identifying key events. Using a dataset N = 1909 collected from multiple clinics equipped with EMBRYOSCOPE/EMBRYOSCOPE+ (Vitrolife), GERI (Genea Biomedx) MIRI (Esco Medical), this study proposes novel architecture automatically detect 11 kinetic events (from 1-cell blastocyst). First, Transformer video backbone was trained custom metric inspired by reverse cross-entropy which enables model learn ordinal structure Second, embeddings were extracted and passed into Gated Recurrent Unit (GRU) sequence account dependencies. A weighted average 66.0%, 67.6% 66.3% timing precision, recall F1-score respectively reached on test set 278 embryos, applicable TLS.

Language: Английский

Citations

4
Opportunities and limitations of introducing artificial intelligence technologies into reproductive medicine DOI Creative Commons

V. A. Lebina,

O. Kh. Shikhalakhova,

A. A. Kokhan

et al.

Obstetrics Gynecology and Reproduction, Journal Year: 2025, Volume and Issue: unknown

Published: March 14, 2025

Given the increasing problem of infertility in Russian Federation, assisted reproductive technologies (ART) have proven to be one most effective treatments for this condition. Notably, introduction ART methods, particularly vitro fertilization (IVF), has led markedly increased birth rates over past two decades. Studies show that machine learning algorithms can process images embryos assess their quality, thus facilitating selection viable among them transfer. There are ethical and technical barriers hindering widespread adoption artificial intelligence (AI) clinical practice, including concerns data privacy as well a need train specialists deal with new technologies. AI analyze vast amounts data, medical histories research results, more accurately predict pregnancy outcomes. This enables doctors make justified decisions. In future, will able patient efficiently, helping identify causes at earlier stages.

Language: Английский

Citations

0
Human embryo stage classification using an enhanced R(2 + 1)D model and dynamic programming with optimized datasets DOI

Abbas Barhoun,

Mohammad Ali Balafar, Amin Golzari Oskouei

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107841 - 107841

Published: March 28, 2025

Language: Английский

Citations

0
A review of artificial intelligence applications in in vitro fertilization DOI
Qing Zhang, Xiaowen Liang, Zhiyi Chen

et al.

Journal of Assisted Reproduction and Genetics, Journal Year: 2024, Volume and Issue: 42(1), P. 3 - 14

Published: Oct. 14, 2024

Language: Английский

Citations

2
Artificial intelligence in human reproduction DOI
Gerardo Mendizabal‐Ruiz, Omar Paredes,

Ángel Álvarez

et al.

Archives of Medical Research, Journal Year: 2024, Volume and Issue: 55(8), P. 103131 - 103131

Published: Nov. 29, 2024

Language: Английский

Citations

1
Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications DOI Creative Commons
Carlo Bulletti, Jason M. Franasiak, Andrea Busnelli

et al.

Mayo Clinic Proceedings Digital Health, Journal Year: 2024, Volume and Issue: 2(4), P. 518 - 532

Published: Aug. 27, 2024

The aim of this systematic review was to identify clinical decision support algorithms (CDSAs) proposed for assisted reproductive technologies (ARTs) and evaluate their effectiveness in improving ART cycles at every stage vs traditional methods, thereby providing an evidence-based guidance use practice. A literature search on PubMed Embase articles published between 1 January 2013 31 2024 performed relevant articles. Prospective retrospective studies English the CDSA were included. Out 1746 screened, 116 met inclusion criteria. selected categorized into 3 areas: prognosis patient counseling, management, embryo assessment. After screening, 11 CDSAs identified as potentially valuable management laboratory practices. Our findings highlight potential automated aids improve vitro fertilization outcomes. However, main limitation lack standardization validation methods across studies. Further trials are needed establish these tools setting.

Language: Английский

Citations

0
Investigating the Artificial intelligence in prediction and evaluation sperm and embryo quality in In vitro fertilization (IVF): A systematic review DOI Creative Commons

shahrzad kaveh,

Aida Ghafari,

zahra khedri

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 25, 2024

Abstract Importance: Assisted Reproductive Technologies (ART) have been developed to address infertility by improving embryo selection. Artificial intelligence (AI), using Time-Lapse Imaging (TLI), enhances predictions from fertilization the blastocyst stage. Objective: Studies show AI can identify suitable embryos more effectively than specialists, in-vitro (IVF) success rates enhancing transfer and reducing miscarriage risks. With IVF below 40%, it is essential explore methods boost outcomes. Findings: A systematic review in October 2024 searched databases like PubMed Scopus terms related AI, excluding non-English qualitative studies. Twenty-seven studies were reviewed; 17 predicted treatment responses with deep learning. Two used neural networks for successful prediction, eight employed ML such as NB, SVM, RF, an average AUC of 0.91. Models showed 90-96% accuracy, sensitivity, precision. Conclusion: technologies, particularly NB Reinforcement Learning, promise outcomes classification diagnosis while saving time. Interdisciplinary approaches micro Nano-biotechnology help overcome clinical challenges. Relevance: Examining quality sperm egg separately could further improve fertility testing ART, optimizing results.

Language: Английский

Citations

0