ArticleReproductive medicine and biology
A novel system based on artificial intelligence for predicting blastocyst viability and visualizing the explanation.
Article in Reproductive medicine and biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- FertilitY Predictor-a machine learning-based web tool for the prediction of assisted reproduction outcomes in men with Y chromosome microdeletions.Journal of assisted reproduction and genetics · 2025Pooled it
- "Old and new" frontiers in embryo viability assessment: current practices and introduction of emerging approaches.Biology of reproduction · 2026Review
- Explainable Artificial Intelligence in Assisted Reproductive Technology: Bridging Prediction and Clinical Judgment.Biomedicines · 2026Review
- Investigating discrepancies in accuracy, agreement and interpretability for single-frame embryo classification tasks conducted by embryologists and deep learning models.Frontiers in reproductive health · 2026Article
- The Influencing Factors and Predictive Algorithm of Pregnancy Outcomes in IVF/ICSI-ET Patients.International journal of women's health · 2026Article
- Impact of rapidly cleaving embryos on blastocyst formation and single-blastocyst transfer outcomes.Frontiers in endocrinology · 2026Article
- Development and evaluation of clinical pregnancy prediction models for intrauterine insemination using three machine learning algorithms.Frontiers in endocrinology · 2026Article
- Multi-modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review.Annals of biomedical engineering · 2026Review
- Multidimensional trophoblast invasion assessment by combining 3D in vitro modeling and deep learning analysis.NPJ systems biology and applications · 2025Article
- Application of Large Language Models in Data Analysis and Medical Education for Assisted Reproductive Technology: Comparative Study.JMIR formative research · 2025Article
- Predicting pregnancy outcomes in IVF cycles: a systematic review and diagnostic meta- analysis of artificial intelligence in embryo assessment.Contraception and reproductive medicine · 2025Review
- Identification of novel COL4A5 variants and prenatal diagnosis in three large families.Scientific reports · 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
- Advanced KPI framework for IVF pregnancy prediction models in IVF protocols.Scientific reports · 2024Article
- 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
- Segmentation of mature human oocytes provides interpretable and improved blastocyst outcome predictions by a machine learning model.Scientific reports · 2024Article
- A brief history of artificial intelligence embryo selection: from black-box to glass-box.Human reproduction (Oxford, England) · 2024Review
- Does cleavage stage morphology increase the discriminatory power of prediction in blastocyst transfer outcome?Journal of assisted reproduction and genetics · 2024Article
- Bovine embryo productionAnimal reproduction · 2024Review
- An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization.Scientific data · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The purpose of the study was to invent and evaluate the novel artificial intelligence (AI) system named Fertility image Testing Through Embryo (FiTTE) for predicting blastocyst viability and visualizing the explanations via gradient-based localization. Methods: The authors retrospectively analyzed 19 342 static blastocyst images with related inspection histories from 9961 infertile patients who underwent in vitro fertilization. Among these data, 17 984 cycles of single-blastocyst transfer were used for training, and data from 1358 cycles were used for testing purposes. Results: The prediction accuracy for clinical pregnancy achieved by a control model using conventional Gardner scoring system was 59.8%, and area under the curve (AUC) was 0.62. FiTTE improved the prediction accuracy by using blastocyst images to 62.7% and AUC of 0.68. Additionally, the accuracy achieved by an ensemble model using image plus clinical data was 65.2% and AUC was 0.71, representing an improvement in prediction accuracy. The visualization algorithm showed brighter colors with blastocysts that resulted in clinical pregnancy. Conclusions: The authors invented the novel AI system, FiTTE, which could provide more precise prediction of the probability of clinical pregnancy using blastocyst images secondary to single embryo transfer than the conventional Gardner scoring assessments. FiTTE could also provide explanation of AI prediction using colored blastocyst images.
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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.