Evidence map›Paper›PMID 31218565›Full record

ArticleJournal of assisted reproduction and genetics2019

An artificial neural network for the prediction of assisted reproduction outcome.

Paraskevi Vogiatzi, Abraham Pouliakis, Charalampos Siristatidis

Abstract read
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In one paragraph

Article in Journal of assisted reproduction and genetics, 2019. 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.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  14. Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2022
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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

3 authors.

Paraskevi VogiatziAssisted Reproduction Unit, Third Department of Obstetrics and Gynecology, Medical School, "Attikon" University Hospital, National and Kapodistrian University of Athens, 1 Rimini Street, Chaidari, 12642, Athens, Greece.ORCID http://orcid.org/0000-0001-9971-1619
Abraham PouliakisSecond Department of Pathology, Medical School, "Attikon" University Hospital, National and Kapodistrian University of Athens, 1 Rimini Street, Chaidari, 12642, Athens, Greece.
Charalampos SiristatidisAssisted Reproduction Unit, Third Department of Obstetrics and Gynecology, Medical School, "Attikon" University Hospital, National and Kapodistrian University of Athens, 1 Rimini Street, Chaidari, 12642, Athens, Greece. harrysiri@yahoo.gr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo construct and validate an efficient artificial neural network (ANN) based on parameters with statistical correlation to live birth, to be used as a comprehensive tool for the prediction of the clinical outcome for patients undergoing ART.

methodsData from 257 infertile couples that underwent a total of 426 IVF/ICSI cycles from 2010 to 2017 was collected on an ensemble of 118 parameters for each cycle. Statistical correlation of the parameters with the outcome of live birth was performed, using either t test or χ

results12 statistically significant parameters out of the initial ensemble were used for the ANN construction, which exhibited a cumulative sensitivity and specificity of 76.7% and 73.4%, respectively. During cross-validation, the system exhibited the following: sensitivity 69.2% ± 2.36%, specificity 69.19% ± 2.8% (OR 5.21 ± 1.27), PPV 36.96 ± 3.44, NPV 89.61 ± 1.09, and OA 69.19% ± 2.69%. A rather small standard deviation in the performance indices between the training and test sets throughout the validation process indicated a stable performance of the constructed ANN.

conclusionsThe constructed ANN is based on statistically significant variables with the outcome of live birth and represents a stable and efficient system with increased performance indices. Validation of the system allowed an insight of its clinical value as a supportive tool in medical decisions, and overall provides a reliable approach in the routine practice of IVF units in a user-friendly environment.

Indexed as

Neural Networks, ComputerAdultBirth RateFemaleFertilization in VitroHumansInfertilityLive BirthMalePrecision MedicinePregnancyPregnancy OutcomePregnancy RateReproductive Techniques, AssistedSperm Injections, IntracytoplasmicArtificial intelligenceArtificial neural networkAssisted reproductionLive birthPersonalized treatmentPrediction model

Identifiers

PMID31218565
PMCPMC6642243

What OpenQuestion holds

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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.