The Role of Artificial Intelligence in The Prediction and Early Diagnosis of Meconium Aspiration Syndrome in Post-Term Newborns | International Journal of Medical Sciences And Clinical Research

Authors

Sagdullaeva Mafura Abdukarimovna

Scientific Supervisor, PhD, Senior Lecturer, Candidate of Medical Sciences of Department “Propaedeutics Of Children's Diseases No.1”, Tashkent State Medical University, Tashkent, Uzbekistan
Abduvaliyev Abror Akbarovich

4nd-year student, Faculty of General Medicine, Tashkent State Medical University Tashkent, Uzbekistan
Iskandar Islamovich Sagdullayev

Assistant Lecturer, Department of Hygiene of Children, Adolescents and Nutrition, Tashkent State Medical University, Tashkent, Uzbekistan

DOI:

https://doi.org/10.37547/ijmscr/Volume06Issue10-02

Keywords:

Meconium aspiration syndrome post-term pregnancy newborns

Abstract

Meconium Aspiration Syndrome (MAS) is one of the most severe respiratory complications in the perinatal period, occurring with particularly high frequency among newborns from post-term pregnancies. The development of this syndrome is associated with chronic placental insufficiency, fetal hypoxia, and oligohydramnios, which are characteristic of pregnancies at 42 weeks of gestation and beyond.

The aim of the present study is to analyze the role of artificial intelligence technologies in the prediction and early diagnosis of meconium aspiration syndrome in post-term newborns.

The study examines the etiology, pathogenesis, and clinical-propaedeutic features of MAS, as well as the capabilities and limitations of traditional diagnostic methods. It is demonstrated that the application of machine learning models and neural networks for processing cardiotocography data, ultrasound findings, and clinical-laboratory parameters can significantly improve the accuracy of predicting the risk of severe forms of the syndrome and optimize the management tactics of post-term pregnancy.

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