A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Cavalera, Federica; Zanoni, Mario; Merico, Valeria; Bui, Thi Thu Hien; Belli, Martina; Fassina, Lorenzo; Garagna, Silvia; Zuccotti, Maurizio

doi:10.3791/56668-v

Cited by 5 publications

(4 citation statements)

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“…When implemented into a smartphone, AI could help assess sperm quality at home with an accuracy of 88.5%, without the need for professional technicians 145 . An artificial neural network has been used to analyze TLP images of cytoplasmic movements to predict the competency of mouse oocytes, with an accuracy of 91% 146 . Detection of the extruded polar body using machine learning allows for developmental stage definition and timely ICSI preparation.…”

Section: Discussionmentioning

confidence: 99%

“…145 An artificial neural network has been used to analyze TLP images of cytoplasmic movements to predict the competency of mouse oocytes, with an accuracy of 91%. 146 Detection of the extruded polar body using machine learning allows for developmental stage definition and timely ICSI preparation. Current hotspots of AI applications in embryo management can be categorized into the following aspects: automatic annotation of embryo development, embryo grading, and embryo selection for implantation.…”

Section: Integration Of Artificial Intelligence In Microfluidicsmentioning

confidence: 99%

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Microfluidic chip as a promising evaluation method in assisted reproduction: A systematic review

Wu,

Yan

et al. 2023

Bioengineering & Transla Med

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The aim of assisted reproductive technology (ART) is to select the high‐quality sperm, oocytes, and embryos, and finally achieve a successful pregnancy. However, functional evaluation is hindered by intra‐ and inter‐operator variability. Microfluidic chips emerge as the one of the most powerful tools to analyze biological samples for reduced size, precise control, and flexible extension. Herein, a systematic search was conducted in PubMed, Scopus, Web of Science, ScienceDirect, and IEEE Xplore databases until March 2023. We displayed and prospected all detection strategies based on microfluidics in the ART field. After full‐text screening, 71 studies were identified as eligible for inclusion. The percentages of human and mouse studies equaled with 31.5%. The prominent country in terms of publication number was the USA (n = 13). Polydimethylsiloxane (n = 49) and soft lithography (n = 28) were the most commonly used material and fabrication method, respectively. All articles were classified into three types: sperm (n = 38), oocytes (n = 20), and embryos (n = 13). The assessment contents included motility, counting, mechanics, permeability, impedance, secretion, oxygen consumption, and metabolism. Collectively, the microfluidic chip technology facilitates more efficient, accurate, and objective evaluation in ART. It can even be combined with artificial intelligence to assist the daily activities of embryologists. More well‐designed clinical studies and affordable integrated microfluidic chips are needed to validate the safety, efficacy, and reproducibility.Trial registration: The protocol was registered in the Open Science Frame REGISTRIES (identification: osf.io/6rv4a).

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Section: Discussionmentioning

confidence: 99%

Section: Integration Of Artificial Intelligence In Microfluidicsmentioning

confidence: 99%

Microfluidic chip as a promising evaluation method in assisted reproduction: A systematic review

Wu,

Yan

et al. 2023

Bioengineering & Transla Med

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“…The ways of determining these potential indicators are invasive, time‐consuming, and expensive. Attempts have also been made to identify the physical indicators of the oocyte developmental potential, including cytoplasmic movement, impedance, membrane permeability, zona pellucida rigidity, and cytoplasmic viscosity (Cavalera et al, 2018; Chen et al, 2018; Fernandez et al, 2015; Kort & Behr, 2017). The implementation in practice limits the development of an enhanced indicator by integrating the abovementioned single physical indicators whose effectiveness remains uncertain.…”

Section: Introductionmentioning

confidence: 99%

Identification of significant imaging features for sensing oocyte viability

Chen

Liu

Zuo

et al. 2022

Microscopy Res & Technique

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The evaluation of oocyte viability in the laboratory is limited to the morphological assessment by naked eyes, but the realization that most normal-appearing oocytes may conceal abnormalities prompts the search for automated approaches that can detect the abnormalities imperceptible to naked eyes. In this study, we developed an image processing pipeline applicable to bright-field microscope images to quantify the causal relationship between the quantitative imaging features and the developmental potential of oocytes. We acquired 19 imaging features of approximately 700 oocytes and determined two imaging subtypes, namely viable and nonviable subtypes that correlated closely with a viability fluorescence indicator and cleavage rates. The causal relationship between these imaging features and oocyte viability was derived from a viability-oriented Bayesian network that was developed based on the Bayesian information criterion and Tabu search. Our experimental results revealed that entropy with mean Gray Level Co-Occurrence Matrix energy describing the uniformity and texture roughness of cytoplasm were salient features for the automated selection of promising oocytes that exhibited excellent developmental potential.

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“…G. POLAT, H. K. ARSLAN vitro 3 . The Cytoplasmic Movement Velocities of each oocyte were evaluated with a mathematical classification tool and the probability of being developmentally sufficient or insufficient could be estimated with 91.03% accuracy.…”

mentioning

confidence: 99%

Artificial Intelligence in Clinical and Surgical Gynecology

Polat,

Arslan

2024

İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi

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Clinicians have increasingly been using artificial intelligence (AI) to make decisions and to increase their knowledge in various clinical and surgical gynecological areas. A vast amount of clinical, medical, and biological patient data is processed in fast computer networks using complex algorithms to create mathematical modeling. The development of these mathematical models gives hope of a promising future with their contribution to overcoming the difficulties encountered in the diagnosis, individualization of treatment plans and improving patient outcomes. Virtual AI in clinical gynecology uses pattern recognition to aid diagnosis, plan treatment, and predict outcomes in gynecological malignancies, assisted reproductive techniques, and urogynecology. In gynecological surgery, physical AI combines augmented reality in operations in the form of computer-aided or robotic platforms. However, AI is yet to be fully incorporated into modern medical practice to improve patient outcomes in clinical gynecology.

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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Cited by 5 publications

References 0 publications

Microfluidic chip as a promising evaluation method in assisted reproduction: A systematic review

Microfluidic chip as a promising evaluation method in assisted reproduction: A systematic review

Identification of significant imaging features for sensing oocyte viability

Artificial Intelligence in Clinical and Surgical Gynecology

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