ArticleHeliyon2021
Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality.
Article in Heliyon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning applications for human embryo assessment using time-lapse imaging: scoping review.Frontiers in reproductive health · 2025Pooled it
- An Optimal Approach in Selecting Embryo for In-Vitro Fertilization (IVF) Based on Deep Learning.Journal of biomedical physics & engineering · 2026Article
- AI-enabled precision reproductive medicine: a comprehensive review of clinical applications, decision frameworks, and evidence-based implementation.Journal of assisted reproduction and genetics · 2026Review
- Contemporary Approaches Towards the Optimization of Embryo Implantation.Journal of clinical medicine · 2026Review
- Stability and reliability of artificial intelligence models in embryo selection for in vitro fertilization.Fertility and sterility · 2026Article
- Ensemble deep learning with advanced feature engineering for embryo evaluation on in-vitro fertilisation procedures using biomedical images.Scientific reports · 2025Article
- Deep Learning Models: Their Relationship with Embryonic Euploidies and Reproductive Outcomes.Genes · 2025Review
- Cleavage-stage embryo segmentation using SAM-based dual branch pipeline: development and evaluation with the CleavageEmbryo dataset.Bioinformatics (Oxford, England) · 2025Article
- Merging synthetic and real embryo data for advanced AI predictions.Scientific reports · 2025Article
- Combined Input Deep Learning Pipeline for Embryo Selection for In Vitro Fertilization Using Light Microscopic Images and Additional Features.Journal of imaging · 2025Article
- Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications.Mayo Clinic proceedings. Digital health · 2024Review
- Anomaly Detection in Embryo Development and Morphology Using Medical Computer Vision-Aided Swin Transformer with Boosted Dipper-Throated Optimization Algorithm.Bioengineering (Basel, Switzerland) · 2024Article
- Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization.Nature communications · 2024Article
- Machine learning in time-lapse imaging to differentiate embryos from young vs old mice†.Biology of reproduction · 2024Article
- The prospect of artificial intelligence to personalize assisted reproductive technology.NPJ digital medicine · 2024Review
- Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine.Journal of assisted reproduction and genetics · 2024Review
- Deep learning pipeline reveals key moments in human embryonic development predictive of live birth after in vitro fertilization.Biology methods & protocols · 2024Article
- Accurate staging of chick embryonic tissues via deep learning of salient features.Development (Cambridge, England) · 2023Article
- Embryo ploidy status classification through computer-assisted morphology assessment.AJOG global reports · 2023Article
- An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization.Scientific data · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
A critical factor that influences the success of an in-vitro fertilization (IVF) treatment cycle is the quality of the transferred embryo. Embryo morphology assessments, conventionally performed through manual microscopic analysis suffer from disparities in practice, selection criteria, and subjectivity due to the experience of the embryologist. Convolutional neural networks (CNNs) are powerful, promising algorithms with significant potential for accurate classifications across many object categories. Network architectures and hyper-parameters affect the efficiency of CNNs for any given task. Here, we evaluate multi-layered CNNs developed from scratch and popular deep-learning architectures such as Inception v3, ResNET-50, Inception-ResNET-v2, NASNetLarge, ResNeXt-101, ResNeXt-50, and Xception in differentiating between embryos based on their morphological quality at 113 h post insemination (hpi). Xception performed the best in differentiating between the embryos based on their morphological quality.
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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.