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The Fetal Medicine
Foundation
Computational Intelligent Diagnostic System in
Predicting Fetal Aneuploidies
Computational Intelligence
Christos Schizas
Kypros Nicolaides
Andreas Neocleous
Kleanthis Neokleous
Natasa Schiza
Costas Neocleous
• Artificial neural networks
• Evolutionary systems /
Genetic algorithms
• Artificial immune systems
• Fuzzy 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
FMF, University of Cyprus, Cyprus University of Technology, Cyprus
Computational Intelligent System
in predicting fetal aneuploidies
The Fetal Medicine
Foundation
Objective: Employ computational intelligence to predict fetal aneuploidies
Artificial Neural Network Architecture
Input (10 neurons)
All data:
Total singleton pregnancies
Euploid
Aneuploidy
Trisomy 21
Trisomy 18
Trisomy 13
Triploidy
Turner syndrome
34,182
33,792 (98.8%)
390 (1.2%)
213
97
27
18
35
Data for training, simulations and validations:
Training various artificial neural networks 26,000
Totally unknown cases used for validations 8,182
Age, previous trisomy, CRL, NT, FHR,
ß-hCG, PAPP-A, NB, TR, DV
(Linear activation)
Hidden Layer 1 (80 neurons)
(Logistic activation)
Hidden Layer 2 (10 neurons)
(Symmetric logistic activation)
Hidden Layer 3 (80 neurons)
(Logistic activation)
Output Layer (5 neurons)
Normal / Abnormal (Turner, T13,T18,T21)
(Logistic activation)
The Fetal Medicine
Foundation
Computational Intelligent System
in predicting fetal aneuploidies
Results on the unknown validation (verification) data set:
Classification into EUPLOID - ANEUPLOID
ALL cases
Predicted Correct
Euploid
Aneuploid
8,032
64
8,017 (99.8%)
64 (100%)
Classification into EUPLOID – Trisomy 21
ALL cases
Predicted Correct
Euploid
Trisomy 21
8,032
60
8,016 (99.8%)
54 (90.0%)
Classification into EUPLOID - T21 – T18 - T13 – Triploidy - Turner
ALL cases
Predicted Correct
4,521
Normal
Trisomy 21
Trisomy 18
Trisomy 13
Triploidy
Turner
4,482
21
10
3
2
3
18
(85.7%)
6
(60.0%)
0
0
0
4,505
4,481
(99.5%) (99.98%)
The Fetal Medicine
Foundation
Computational Intelligent System
in predicting fetal aneuploidies
Conclusions
 There is a very good discrimination between Euploid and Aneuploid cases
 There is a good discrimination between normal and Trisomy 21 cases
 T13, Triploidy and Turner cases are hard to predict (mainly because of the small
number of cases available for network training)
Thank you