ReviewJournal of assisted reproduction and genetics2021
Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF.
Review in Journal of assisted reproduction and genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 57 citations in OpenAlex.
- Artificial intelligence in reproductive medicine and education: current evidence, challenges, and future directions.Journal of assisted reproduction and genetics · 2026Review
- Artificial intelligence-powered evaluation model for English translation education in university: combining quantitative and qualitative methods.Scientific reports · 2026Article
- Multi-modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review.Annals of biomedical engineering · 2026Review
- Artificial Intelligence in Routine IVF Practice.Biology · 2025Review
- FERTILITY CARE IN LOW- AND MIDDLE- INCOME COUNTRIES: The future use of AI to improve accessibility of assisted reproductive technology in low- and middle-income countries.Reproduction & fertility · 2025Review
- Merging synthetic and real embryo data for advanced AI predictions.Scientific reports · 2025Article
- A review of artificial intelligence applications in in vitro fertilization.Journal of assisted reproduction and genetics · 2025Review
- Dev-ResNet: automated developmental event detection using deep learning.The Journal of experimental biology · 2024Article
- EVATOM: an optical, label-free, machine learning assisted embryo health assessment tool.Communications biology · 2024Article
- Catalyzing IVF outcome prediction: exploring advanced machine learning paradigms for enhanced success rate prognostication.Frontiers in artificial intelligence · 2024Article
- In Contemporary Reproductive Medicine Human Beings are Not Yet Dispensable.Journal of obstetrics and gynaecology of India · 2023Article
- Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.PLoS computational biology · 2023Article
- Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature.Computational and structural biotechnology journal · 2023Article
- DTLR-CS: Deep tensor low rank channel cross fusion neural network for reproductive cell segmentation.PloS one · 2023Article
- Prediction model of gonadotropin starting dose and its clinical application in controlled ovarian stimulation.BMC pregnancy and childbirth · 2022Article
- Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic Analysis.Biomedicines · 2022Article
- Unique Deep Radiomic Signature Shows NMN Treatment Reverses Morphology of Oocytes from Aged Mice.Biomedicines · 2022Article
- Machine Learning-Based Modeling of Ovarian Response and the Quantitative Evaluation of Comprehensive Impact Features.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In vitro fertilization has been regarded as a forefront solution in treating infertility for over four decades, yet its effectiveness has remained relatively low. This could be attributed to the lack of advancements for the method of observing and selecting the most viable embryos for implantation. The conventional morphological assessment of embryos exhibits inevitable drawbacks which include time- and effort-consuming, and imminent risks of bias associated with subjective assessments performed by individual embryologists. A combination of these disadvantages, undeterred by the introduction of the time-lapse incubator technology, has been considered as a prominent contributor to the less preferable success rate of IVF cycles. Nonetheless, a recent surge of AI-based solutions for tasks automation in IVF has been observed. An AI-powered assistant could improve the efficiency of performing certain tasks in addition to offering accurate algorithms that behave as baselines to minimize the subjectivity of the decision-making process. Through a comprehensive review, we have discovered multiple approaches of implementing deep learning technology, each with varying degrees of success, for constructing the automated systems in IVF which could evaluate and even annotate the developmental stages of an embryo.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.