Arrangement of risk group with congenital fetus pathology using artificial neural nets
The aim of the study is the evaluation of the significance of various risk factors for congenital fetus pathology (congenital defect – CD) and the development of risk numeric scale. 424 pregnant women with fetus CD and 520 pregnant women with fetus without congenital defects have been examined. Arti...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Siberian State Medical University (Tomsk)
2003-06-01
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| Series: | Бюллетень сибирской медицины |
| Subjects: | |
| Online Access: | https://bulletin.ssmu.ru/jour/article/view/3765 |
| _version_ | 1827992064362545152 |
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| author | O. S. Philippov A. A. Kazantseva |
| author_facet | O. S. Philippov A. A. Kazantseva |
| author_sort | O. S. Philippov |
| collection | DOAJ |
| description | The aim of the study is the evaluation of the significance of various risk factors for congenital fetus pathology (congenital defect – CD) and the development of risk numeric scale. 424 pregnant women with fetus CD and 520 pregnant women with fetus without congenital defects have been examined. Artificial neural nets have been used for investigation how various factors effect on pregnancy termination. It has been found that the important factors for congenital fetus defect risk are: age younger 18 years of an pregnant women, age older 35 years, noncarring of pregnancy in anamnesis, complicated clinical course of the first pregnancy trimester, CD cases in a family, ultrasonic markers of chromosome pathology in the first pregnancy trimester. Changes in maternal serum AFP, hCG and uE3 levels and blood flow disorders are important to forming high risk group. A numeric scale for CD risk has been developed on the basis of neuronic net analysis. |
| first_indexed | 2024-04-10T01:15:03Z |
| format | Article |
| id | doaj.art-33238dc844684b5ea68e3ef0b11505db |
| institution | Directory Open Access Journal |
| issn | 1682-0363 1819-3684 |
| language | English |
| last_indexed | 2024-04-10T01:15:03Z |
| publishDate | 2003-06-01 |
| publisher | Siberian State Medical University (Tomsk) |
| record_format | Article |
| series | Бюллетень сибирской медицины |
| spelling | doaj.art-33238dc844684b5ea68e3ef0b11505db2023-03-13T09:58:02ZengSiberian State Medical University (Tomsk)Бюллетень сибирской медицины1682-03631819-36842003-06-012210310910.20538/1682-0363-2003-2-103-1092385Arrangement of risk group with congenital fetus pathology using artificial neural netsO. S. Philippov0A. A. Kazantseva1Красноярская государственная медицинская академияМежрегиональный Красноярский диагностический центр медицинской генетикиThe aim of the study is the evaluation of the significance of various risk factors for congenital fetus pathology (congenital defect – CD) and the development of risk numeric scale. 424 pregnant women with fetus CD and 520 pregnant women with fetus without congenital defects have been examined. Artificial neural nets have been used for investigation how various factors effect on pregnancy termination. It has been found that the important factors for congenital fetus defect risk are: age younger 18 years of an pregnant women, age older 35 years, noncarring of pregnancy in anamnesis, complicated clinical course of the first pregnancy trimester, CD cases in a family, ultrasonic markers of chromosome pathology in the first pregnancy trimester. Changes in maternal serum AFP, hCG and uE3 levels and blood flow disorders are important to forming high risk group. A numeric scale for CD risk has been developed on the basis of neuronic net analysis.https://bulletin.ssmu.ru/jour/article/view/3765врожденные пороки развитияпренатальная диагностиканейронные сети |
| spellingShingle | O. S. Philippov A. A. Kazantseva Arrangement of risk group with congenital fetus pathology using artificial neural nets Бюллетень сибирской медицины врожденные пороки развития пренатальная диагностика нейронные сети |
| title | Arrangement of risk group with congenital fetus pathology using artificial neural nets |
| title_full | Arrangement of risk group with congenital fetus pathology using artificial neural nets |
| title_fullStr | Arrangement of risk group with congenital fetus pathology using artificial neural nets |
| title_full_unstemmed | Arrangement of risk group with congenital fetus pathology using artificial neural nets |
| title_short | Arrangement of risk group with congenital fetus pathology using artificial neural nets |
| title_sort | arrangement of risk group with congenital fetus pathology using artificial neural nets |
| topic | врожденные пороки развития пренатальная диагностика нейронные сети |
| url | https://bulletin.ssmu.ru/jour/article/view/3765 |
| work_keys_str_mv | AT osphilippov arrangementofriskgroupwithcongenitalfetuspathologyusingartificialneuralnets AT aakazantseva arrangementofriskgroupwithcongenitalfetuspathologyusingartificialneuralnets |