ArticleJournal of assisted reproduction and genetics2021
Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory.
Article 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. An erratum has been issued. Cited by 19 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
19 citing papers in PubMed.
- Construction and validation of a preimplantation kinship identification model using ultra-low-depth whole genome sequencing.iScience · 2026Article
- Personal KPIs in IVF Laboratory: Are They Measurable or Distortable? A Case Study Using AI-Based Benchmarking.Journal of clinical medicine · 2025Article
- Key Performance Indicators Applied in Medicine.International archives of otorhinolaryngology · 2025Article
- Deep learning classification integrating embryo images with associated clinical information from ART cycles.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
- A deep learning approach to understanding controlled ovarian stimulation and in vitro fertilization dynamics.Scientific reports · 2025Article
- Criteria for implementing artificial intelligence systems in reproductive medicine.Clinical and experimental reproductive medicine · 2024Article
- Culture conditions in the IVF laboratory: state of the ART and possible new directions.Journal of assisted reproduction and genetics · 2023Review
- Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.Nature communications · 2023Review
- Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI).Journal of assisted reproduction and genetics · 2023Article
- Proceedings of the first world conference on AI in fertility.Journal of assisted reproduction and genetics · 2023Article
- Embryo selection through artificial intelligence versus embryologists: a systematic review.Human reproduction open · 2023Article
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
- Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study.Journal of assisted reproduction and genetics · 2022Article
- Fine-tuning IVF laboratory key performance indicators of the Vienna consensus according to female age.Journal of assisted reproduction and genetics · 2022Article
- Estradiol pretreatment in GnRH antagonist protocol for IVF/ICSI treatment.Open medicine (Warsaw, Poland) · 2022Article
- Artificial intelligence-the future is now.Journal of assisted reproduction and genetics · 2021Article
- Embryologist staffing in assisted reproductive technology laboratories: An international comparative review.Reproductive medicine and biologyReview
- Evolution of intracytoplasmic sperm injection: From initial challenges to wider applications.Reproductive medicine and biologyReview
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Staff competency is a crucial component of the in vitro fertilization (IVF) laboratory quality management system because it impacts clinical outcomes and informs the key performance indicators (KPIs) used to continuously monitor and assess culture conditions. Contemporary quality control and assurance in the IVF lab can be automated (collect, store, retrieve, and analyze), to elevate quality control and assurance beyond the cursory monthly review. Here we demonstrate that statistical KPI monitoring systems for individual embryologist performance and culture conditions can be detected by artificial intelligence systems to provide systemic, early detection of adverse outcomes, and identify clinically relevant shifts in pregnancy rates, providing critical validation for two statistical process controls proposed in the Vienna Consensus Document; intracytoplasmic sperm injection (ICSI) fertilization rate and day 3 embryo 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.