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 Background The 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 classi...
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| Format: | Article |
| Language: | English |
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BMC
2018-02-01
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| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12920-018-0333-2 |
| _version_ | 1818721798116605952 |
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| author | Aida Catic Lejla Gurbeta Amina Kurtovic-Kozaric Senad Mehmedbasic Almir Badnjevic |
| author_facet | Aida Catic Lejla Gurbeta Amina Kurtovic-Kozaric Senad Mehmedbasic Almir Badnjevic |
| author_sort | Aida Catic |
| collection | DOAJ |
| description | Abstract Background The 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 analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work. Methods The database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology “Mehmedbasic” for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound. Results The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman’s) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%. Conclusion The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes. |
| first_indexed | 2024-12-17T20:44:27Z |
| format | Article |
| id | doaj.art-65bdc94655f947a1b821196f0539cca4 |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-12-17T20:44:27Z |
| publishDate | 2018-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-65bdc94655f947a1b821196f0539cca42022-12-21T21:33:12ZengBMCBMC Medical Genomics1755-87942018-02-0111111210.1186/s12920-018-0333-2Application 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 demographicsAida Catic0Lejla Gurbeta1Amina Kurtovic-Kozaric2Senad Mehmedbasic3Almir Badnjevic4Department of Genetics and Bioengineering, International Burch UniversityDepartment of Genetics and Bioengineering, International Burch UniversityDepartment of Genetics and Bioengineering, International Burch UniversityInstitute for Gynecology, Perinatology and Infertility “Mehmedbasic”Department of Genetics and Bioengineering, International Burch UniversityAbstract Background The 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 analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work. Methods The database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology “Mehmedbasic” for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound. Results The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman’s) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%. Conclusion The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.http://link.springer.com/article/10.1186/s12920-018-0333-2Combined testTrisomyFetal aneuploidyPrenatal diagnosisArtificial neural networksFeedforward neural network |
| spellingShingle | Aida Catic Lejla Gurbeta Amina Kurtovic-Kozaric Senad Mehmedbasic Almir Badnjevic 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 BMC Medical Genomics Combined test Trisomy Fetal aneuploidy Prenatal diagnosis Artificial neural networks Feedforward neural network |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Combined test Trisomy Fetal aneuploidy Prenatal diagnosis Artificial neural networks Feedforward neural network |
| url | http://link.springer.com/article/10.1186/s12920-018-0333-2 |
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