2018
DOI: 10.1186/s12920-018-0333-2
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Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics

Abstract: BackgroundThe usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular an… Show more

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Cited by 86 publications
(15 citation statements)
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“…In recent years, machine learning techniques and especially ANNs in the eld of diagnosis of fetal abnormalities have been provided robust results (19,21,22). But what can accurately demonstrate the capacity of a ANN for early detection of a disease is the optimal ANN architecture (13).…”
Section: -Discussionmentioning
confidence: 99%
“…In recent years, machine learning techniques and especially ANNs in the eld of diagnosis of fetal abnormalities have been provided robust results (19,21,22). But what can accurately demonstrate the capacity of a ANN for early detection of a disease is the optimal ANN architecture (13).…”
Section: -Discussionmentioning
confidence: 99%
“…Regarding the targeted patient, two different contexts were focused on, i.e., postnatal or antenatal diagnosis. Most of the articles (61/68) focused on diagnosis after birth, while 7 studies consisted of prenatal screening for fetal syndromes [12], diseases with chromosomal abnormalities [13], aneuploidies [14][15][16] and trisomy [17,18] based on noninvasive markers (demographics, sonographic markers, maternal blood). The two contexts are referred to in the following sections as "the post-natal studies" and "the prenatal studies".…”
Section: Publication Targetmentioning
confidence: 99%
“…This algorithm is fast and does not require as much tuning as classic backpropagation [20]. ANN are a powerful tool for recognizing complex functional relationships between covariates and response variables via a learning process [20] and are particularly suitable for prediction of medical diagnoses, including diabetes and pre-diabetes [21][22][23][24][25][26][27]. After training, an ANN system can be applied to predict the output from a given input of new data.…”
Section: Introductionmentioning
confidence: 99%