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Date Published: 20/04/2023
Abstract Views: 593
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DOI: https://doi.org/10.51298/vmj.v525i1B.5107
Issue
Vol. 525 No. 1B (2023)
Section
Các bài báo
How to Cite
Nguyễn, T. T. ., Trần, D. C. ., Đặng, A. L. ., Đinh , T. L. ., Ngô , T. A. ., Nguyễn, Đoan T. ., Nguyễn, V. A. ., Tô , T. T. H., Đoàn , V. H. ., Đào , T. H. T. ., Vũ , T. H. ., Đỗ, Đức H. ., Nguyễn, N. S. ., Nguyễn, X. Đại ., & Nguyễn, H. N. . (2023). RESEARCH ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN PRENATAL SCREENING FOR SOME ANEUPLOIDIES (DOWN, EDWARD AND PATAU). Vietnam Medical Journal, 525(1B). https://doi.org/10.51298/vmj.v525i1B.5107

RESEARCH ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN PRENATAL SCREENING FOR SOME ANEUPLOIDIES (DOWN, EDWARD AND PATAU)

Thị Trang Nguyễn1,2, Danh Cường Trần1,3, Anh Linh Đặng3, Thúy Linh Đinh 4, Toàn Anh Ngô 3, Đoan Trang Nguyễn1, Việt Anh Nguyễn1, Thị Thu Hà Tô 1, Việt Hà Đoàn 1, Thị Huyền Trang Đào 1, Thu Hương Vũ 1, Đức Huy Đỗ1, Ngọc Sơn Nguyễn1, Xuân Đại Nguyễn1, Hoàng Nam Nguyễn1
1 Hanoi medical university
2 Hanoi medical university hospital
3 National hospital of obstetrics and gynecology
4 Hanoi Obstetrics & Gynecology Hospital,

Main Article Content

Abstract

Objective: Evaluate the results of a trial of an artificial intelligence software system designed to support prenatal screening for chromosomal abnormalities (Down, Edward, and Patau) at at the National hospital of Obstetrics and Gynecology and the Hanoi Obstetrics & Gynecology hospital. Subjects and methods: Medical records, including ultrasound results and prenatal screening test results based on maternal blood biochemistry (Double test, Triple test) of at least 100 pregnant women who attended the two hospitals from May 2022 to November 2022, were analyzed using an artificial intelligence software system (machine learning software and expert knowledge-based software). The results were compared with the results of the invasive prenatal testing via amniotic fluid chromosomal analysis (karyotype analysis). Results: The artificial intelligence software system for prenatal screening of chromosomal abnormalities achieved high accuracy, with a sensitivity of 100% and specificity ranging from 80% to 100%. Conclusion: Therefore, this software system is an effective tool for evaluating the risk of common chromosomal abnormalities and has the potential to support clinical decision-making in prenatal screening in the future.

Article Details

Keywords

Prenatal screening, Trisomy, artificial intelligence, machine learning software, expert knowledge system software.

References

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