ArticleScientific reports2020
Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 77 citations in OpenAlex.
- Automated Cytoplasmic Image Analysis System for Non-invasive Oocyte Quality Screening Workflow Using Static Bright-Field Images.Reproductive sciences (Thousand Oaks, Calif.) · 2026Article
- Artificial Intelligence in Embryo Selection: Current Approaches and Clinical Implications.Bioengineering (Basel, Switzerland) · 2026Review
- A data-driven and interpretable hybrid quantum-classical model for male infertility risk assessment.Frontiers in medical technology · 2026Article
- Ensemble deep learning with advanced feature engineering for embryo evaluation on in-vitro fertilisation procedures using biomedical images.Scientific reports · 2025Article
- Current progress and open challenges for applying artificial intelligence across thePatterns (New York, N.Y.) · 2025Review
- MAIA platform for routine clinical testing: an artificial intelligence embryo selection tool developed to assist embryologists.Scientific reports · 2025Article
- Morphometric Blastocyst Assessment: A Retrospective Study Examining the Relationship Between Blastocyst Diameter and Area and Pregnancy Outcomes in Assisted Reproduction Technology Cycles.Journal of clinical medicine · 2025Article
- Advanced strategies for single embryo selection in assisted human reproduction: A review of clinical practice and research methods.Clinical and experimental reproductive medicine · 2025Article
- A Combination of Artificial Intelligence with Genetic Algorithms on Static Time-Lapse Images Improves Consistency in Blastocyst Assessment, An Interpretable Tool to Automate Human Embryo Evaluation: A Retrospective Cohort Study.International journal of fertility & sterility · 2024Article
- 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
- BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.Human reproduction (Oxford, England) · 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
- An artificial intelligence algorithm to select most viable embryos considering current process in IVF labs.Frontiers in artificial intelligence · 2024Article
- A hybrid feature selection algorithm to determine effective factors in predictive model of success rate for in vitro fertilization/intracytoplasmic sperm injection treatment: A cross-sectional study.International journal of reproductive biomedicine · 2023Article
- Using Unlabeled Information of Embryo Siblings from the Same Cohort Cycle to Enhance In Vitro Fertilization Implantation Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Embryo ploidy status classification through computer-assisted morphology assessment.AJOG global reports · 2023Article
- In Contemporary Reproductive Medicine Human Beings are Not Yet Dispensable.Journal of obstetrics and gynaecology of India · 2023Article
- Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessing the viability of a blastosyst is still empirical and non-reproducible nowadays. We developed an algorithm based on artificial vision and machine learning (and other classifiers) that predicts pregnancy using the beta human chorionic gonadotropin (b-hCG) test from both the morphology of an embryo and the age of the patients. We employed two high-quality databases with known pregnancy outcomes (n = 221). We created a system consisting of different classifiers that is feed with novel morphometric features extracted from the digital micrographs, along with other non-morphometric data to predict pregnancy. It was evaluated using five different classifiers: probabilistic bayesian, Support Vector Machines (SVM), deep neural network, decision tree, and Random Forest (RF), using a k-fold cross validation to assess the model's generalization capabilities. In the database A, the SVM classifier achieved an F1 score of 0.74, and AUC of 0.77. In the database B the RF classifier obtained a F1 score of 0.71, and AUC of 0.75. Our results suggest that the system is able to predict a positive pregnancy test from a single digital image, offering a novel approach with the advantages of using a small database, being highly adaptable to different laboratory settings, and easy integration into clinical practice.
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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.