Evidence map›Paper›PMID 36374394›Full record

ArticleJournal of assisted reproduction and genetics2023

Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI).

Panagiotis Cherouveim, Victoria S Jiang, Manoj Kumar Kanakasabapathy, Prudhvi Thirumalaraju, Irene Souter, Irene Dimitriadis, Charles L Bormann, Hadi Shafiee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of assisted reproduction and genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Proceedings of the first world conference on AI in fertility.Journal of assisted reproduction and genetics · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Panagiotis CherouveimDivision of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA.ORCID http://orcid.org/0000-0003-0378-5336
Victoria S JiangDivision of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA.ORCID http://orcid.org/0000-0003-0625-1399
Manoj Kumar KanakasabapathyDivision of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0002-0756-8585
Prudhvi ThirumalarajuDivision of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0002-0573-0243
Irene SouterDivision of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA.ORCID http://orcid.org/0000-0003-3123-5438
Irene DimitriadisDivision of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA.
Charles L BormannDivision of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA.ORCID http://orcid.org/0000-0003-3377-972X
Hadi ShafieeDivision of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA. hshafiee@bwh.harvard.edu.ORCID http://orcid.org/0000-0003-2240-7648

Funding

A smartphone-enabled point-of-care HCV Ag diagnostic using catalytic nanoparticles and AI-enhanced image processingR01EB033866 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI SHAFIEE, HADI · 2022 to 2025
$3.3M
Microchip for HBV testing using HIV-infected blood samplesR01AI138800 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI SHAFIEE, HADI · 2019 to 2023
$3.1M
A mobile health diagnostic device for HIV self-testingR33AI140489 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI SHAFIEE, HADI · 2022 to 2023
$1.8M
A mobile health diagnostic device for HIV self-testingR61AI140489 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI SHAFIEE, HADI · 2019 to 2021
$1.6M
NIAID NIH HHS R01 AI138800NIAID NIH HHS R33 AI140489NIAID NIH HHS R61 AI140489NIBIB NIH HHS R01 EB033866
6 · The paper itself

Abstract

purposeDeep learning neural networks have been used to predict the developmental fate and implantation potential of embryos with high accuracy. Such networks have been used as an assistive quality assurance (QA) tool to identify perturbations in the embryo culture environment which may impact clinical outcomes. The present study aimed to evaluate the utility of an AI-QA tool to consistently monitor ART staff performance (MD and embryologist) in embryo transfer (ET), embryo vitrification (EV), embryo warming (EW), and trophectoderm biopsy (TBx).

methodsPregnancy outcomes from groups of 20 consecutive elective single day 5 blastocyst transfers were evaluated for the following procedures: MD performed ET (N = 160 transfers), embryologist performed ET (N = 160 transfers), embryologist performed EV (N = 160 vitrification procedures), embryologist performed EW (N = 160 warming procedures), and embryologist performed TBx (N = 120 biopsies). AI-generated implantation probabilities for the same embryo cohorts were estimated, as were mean AI-predicted and actual implantation rates for each provider and compared using Wilcoxon singed-rank test.

resultsActual implantation rates following ET performed by one MD provider: "H" was significantly lower than AI-predicted (20% vs. 61%, p = 0.001). Similar results were observed for one embryologist, "H" (30% vs. 60%, p = 0.011). Embryos thawed by embryologist "H" had lower implantation rates compared to AI prediction (25% vs. 60%, p = 0.004). There were no significant differences between actual and AI-predicted implantation rates for EV, TBx, or for the rest of the clinical staff performing ET or EW.

conclusionsAI-based QA tools could provide accurate, reproducible, and efficient staff performance monitoring in an ART practice.

Indexed as

Artificial IntelligenceCryopreservationBlastocystEmbryo ImplantationFemaleHumansPregnancyPregnancy RateReproductive Techniques, AssistedRetrospective StudiesArtificial intelligenceCompetencyEmbryo transferEmbryo vitrification and warmingQuality assuranceTrophectoderm biopsy

Identifiers

PMID36374394
PMCPMC9935795

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.