the fetal medicine foundation computational intelligent diagnostic system in predicting fetal...

The Fetal Medicine Foundation

Computational Intelligent Diagnostic System in Computational Intelligent Diagnostic System in Predicting Fetal AneuploidiesPredicting Fetal Aneuploidies

Christos SchizasKypros NicolaidesAndreas Neocleous Kleanthis NeokleousNatasa SchizaCostas Neocleous

FMF, University of Cyprus, Cyprus University of Technology, CyprusFMF, University of Cyprus, Cyprus University of Technology, Cyprus

Computational IntelligenceComputational Intelligence

• Artificial neural networks Artificial neural networks

• Evolutionary systems / Evolutionary systems /

Genetic algorithmsGenetic algorithms

• Artificial immune systemsArtificial immune systems

• Fuzzy systemsFuzzy systems

• Maternal age• Previous trisomy• Crown-rump length• Gestational age• Nuchal translucency• Fetal heart rate• Free ß-hCG• PAPP-A• Nasal bone• Tricuspid flow• Ductus venosus flow


Computational Intelligent SystemComputational Intelligent Systemin predicting fetal aneuploidiesin predicting fetal aneuploidies

ObjectiveObjective: : Employ computational intelligence to predict fetal aneuploidiesEmploy computational intelligence to predict fetal aneuploidies

All data:All data:Total singleton pregnancies Total singleton pregnancies 34,18234,182EuploidEuploid 33,792 (98.8%)33,792 (98.8%)AneuploidyAneuploidy 390 (1.2%) 390 (1.2%)

Trisomy 21 Trisomy 21 213213Trisomy 18Trisomy 18 97 97Trisomy 13Trisomy 13 27 27TriploidyTriploidy 18 18Turner syndromeTurner syndrome 35 35

Data for training, simulations and validations:Data for training, simulations and validations:Training various artificial neural networks 26,000Training various artificial neural networks 26,000Totally unknown cases used for validations 8,182Totally unknown cases used for validations 8,182

Artificial Neural Network ArchitectureArtificial Neural Network Architecture

Input (10 neurons)Age, previous trisomy, CRL, NT, FHR,

ß-hCG, PAPP-A, NB, TR, DV(Linear activation)

Hidden Layer 1 (80 neurons)(Logistic activation)

Output Layer (5 neurons)Normal / Abnormal (Turner, T13,T18,T21)

(Logistic activation)

Hidden Layer 2 (10 neurons)(Symmetric logistic activation)

Hidden Layer 3 (80 neurons)(Logistic activation)


Results on the unknown validation Results on the unknown validation (verification)(verification) data set: data set:

Predicted Correct

ALL cases

EuploidEuploid AneuploidAneuploid

8,0328,032 6464

8,017 (99.8%)8,017 (99.8%) 64 (100%)64 (100%)

Classification into EUPLOID - ANEUPLOIDClassification into EUPLOID - ANEUPLOID

Normal Normal Trisomy 21Trisomy 21 Trisomy 18Trisomy 18 Trisomy 13Trisomy 13 TriploidyTriploidy TurnerTurner

ALL cases 4,521 4,4824,482 2121 1010 33 22 33

Predicted Correct4,505

(99.5%)4,4814,481

(99.98%)(99.98%)1818

(85.7%)(85.7%)66

(60.0%)(60.0%) 00 00 00

Classification into EUPLOID - T21 Classification into EUPLOID - T21 –– T18 - T13 – Triploidy - TurnerT18 - T13 – Triploidy - Turner


Predicted Correct

ALL cases

EuploidEuploid Trisomy 21Trisomy 21

8,0328,032 6060

8,016 (99.8%)8,016 (99.8%) 54 (90.0%)54 (90.0%)

Classification into EUPLOID – Trisomy 21Classification into EUPLOID – Trisomy 21


ConclusionsConclusions

There is a very good discrimination between Euploid and Aneuploid casesThere is a very good discrimination between Euploid and Aneuploid cases

There is a good discrimination between normal and Trisomy 21 casesThere is a good discrimination between normal and Trisomy 21 cases

T13, Triploidy and Turner cases are hard to predict (mainly because of the small T13, Triploidy and Turner cases are hard to predict (mainly because of the small number of cases available for network training)number of cases available for network training)

Thank youThank you


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