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Related Experiment Video

Updated: Aug 22, 2025

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
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A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient

Deepti Verma1, Shweta Agrawal2, Celestine Iwendi3

  • 1Department of Computer Application, SAGE University, Indore 452020, India.

Diagnostics (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an Adaptive Stochastic Gradient Descent Algorithm to detect fetal abnormalities, achieving 98.642% accuracy in identifying nuchal translucency thickening, improving antenatal care.

Keywords:
Adaptive Stochastic Gradient Descent Algorithm (ASGDA)NT (nuchal translucency)fetal abnormalityrisk score evaluation

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Area of Science:

  • Medical Imaging
  • Fetal Medicine
  • Machine Learning in Healthcare

Background:

  • Mid-trimester ultrasound scans are standard in antenatal care, detecting more fetal abnormalities due to technological advancements.
  • Fetal abnormalities, or birth defects, affect approximately 3 in 1000 pregnancies in industrialized nations.
  • Early and accurate detection of fetal anomalies is crucial for timely intervention and improved outcomes.

Purpose of the Study:

  • To propose and evaluate an Adaptive Stochastic Gradient Descent Algorithm for assessing fetal abnormality risk.
  • To specifically test the algorithm's efficacy in classifying anomalies associated with nuchal translucency thickening.
  • To enhance the diagnostic accuracy of fetal anomaly detection during pregnancy.

Main Methods:

  • Development of a novel Adaptive Stochastic Gradient Descent Algorithm tailored for fetal anomaly detection.
  • Application of the algorithm to classify fetal anomalies, focusing on nuchal translucency measurements.
  • Performance evaluation using key metrics: accuracy, recall, precision, and F1-score.

Main Results:

  • The proposed Adaptive Stochastic Gradient Descent Algorithm demonstrated high classification performance.
  • The method achieved a remarkable accuracy of 98.642% in identifying fetal anomalies.
  • Successful classification of anomalies linked with nuchal translucency thickening was confirmed.

Conclusions:

  • The Adaptive Stochastic Gradient Descent Algorithm offers a promising innovative approach for fetal anomaly risk evaluation.
  • This technique can significantly improve the detection rates of specific fetal abnormalities like nuchal translucency thickening.
  • The findings suggest a potential advancement in antenatal screening and diagnostic capabilities.