First Trimester Diagnosis of Trisomy-21 Using Artificial Neural Networks

A chromosomal disorder caused by the presence of all or part of an extra 21st chromosome is known as the Down syndrome, or trisomy 21, or trisomy G. In the last fifteen years it has become possible to observe these features by ultrasound examination in the third month of intrauterine life. About 75% of trisomy 21 fetuses have absent nasal bone. In the present work, neural network schemes that have been applied to a large data base of findings from ultrasounds of fetuses, aiming at generating a predictor for the risk of Down syndrome are reported. A number of feed forward neural structures, both of standard multilayer and multi-slab types were tried to find the best for prediction. The database was composed of 23513 cases of fetuses in UK, provided by the Fetal Medicine Foundation in London. For each pregnant woman, 19 parameters were measured or recorded. Out of these, 11 parameters were considered as the most influential at characterizing the risk for this type of chromosomal defect. The best results obtained were with a multi-slab neural structure. In the training set there was a correct classification of the 98.9% cases of trisomy 21 and in the guidance (test) set 100%. The prediction for the totally unknown verification test set was 93.3%. Keywords— Trisomy 21, Down syndrome, neural networks.