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Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities.

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Summary

This study reviews AI techniques for diagnosing abnormal Amniotic Fluid Volume (AFV) levels, crucial for fetal health. It explores contributing factors and machine learning methods, offering insights into challenges and future research in AFV assessment.

Keywords:
amniotic fluid (AF)artificial intelligencedeep learningmachine learningoligohydramniospolyhydramniosultrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Fetal Medicine

Background:

  • Fetal ultrasound (US) assesses infant development, with Amniotic Fluid Volume (AFV) crucial in the second and third trimesters.
  • Abnormal AFV levels, oligohydramnios or polyhydramnios, pose significant risks to maternal and fetal health.
  • Accurate AFV assessment is vital for monitoring fetal well-being and identifying potential complications.

Purpose of the Study:

  • To review and compare recent Artificial Intelligence (AI) advancements for diagnosing and classifying Amniotic Fluid Volume (AFV) levels.
  • To provide a comprehensive overview of factors contributing to abnormal AFV, including placental, renal, and CNS abnormalities, preterm birth, and twin-to-twin transfusion syndrome.
  • To summarize Machine Learning (ML) and Deep Learning (DL) techniques and datasets used in AFV analysis.

Main Methods:

  • Literature review and synthesis of recent advancements in AI for AFV assessment.
  • Categorization of contributing factors to abnormal AFV levels.
  • Comparative analysis of ML and DL techniques applied to AFV diagnosis.

Main Results:

  • Identification of key AI-based techniques for AFV diagnosis and classification.
  • Detailed examination of various etiological factors linked to abnormal AFV.
  • Overview of ML/DL methodologies and datasets employed in current research.

Conclusions:

  • AI holds significant promise for improving the accuracy and efficiency of AFV assessment.
  • Understanding contributing factors is essential for comprehensive diagnosis and management of AFV disorders.
  • Further research into ML/DL applications and addressing current challenges can enhance fetal health monitoring.