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Related Experiment Video

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Deep Learning-Based Segmentation of Fetal Anatomical Structures in the First Trimester.

Subeen Hong1, Oyoung Kim2, Byung Soo Kang1

  • 1Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

Prenatal Diagnosis
|May 6, 2026
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) system accurately identifies and classifies first-trimester fetal structures using the YOLACT model. This AI shows potential for real-time clinical applications in early anomaly screening.

Keywords:
artificial intelligencedeep learningfirstpregnancy trimester

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Fetal Medicine

Background:

  • First-trimester ultrasound is crucial for early fetal assessment.
  • Accurate identification of fetal structures is essential for anomaly screening.
  • Automating this process can improve efficiency and consistency.

Purpose of the Study:

  • To develop and evaluate an AI system for automatic identification and classification of first-trimester fetal structures.
  • To assess the performance of the YOLACT model for fetal structure segmentation.

Main Methods:

  • Utilized over 20,000 first-trimester ultrasound images from four university hospitals.
  • Annotated fetal structures (head, neck, thorax, abdomen, extremities, spine) based on standardized guidelines.
  • Employed the YOLACT model for real-time instance segmentation and evaluated performance using detection accuracy, mAP, and FPS.

Main Results:

  • Achieved 98.4% overall anatomical detection accuracy.
  • Demonstrated high segmentation performance (F1-score > 0.950) for structures like the cranium and heart.
  • Confirmed real-time processing at 25.4 FPS with a mean average precision (mAP) of 0.622 at IoU 0.5.

Conclusions:

  • The YOLACT-based AI model accurately and efficiently segments first-trimester fetal structures.
  • This AI system shows significant potential for real-time clinical application in early anomaly screening.
  • Further development could enhance recall for structures like the nasal bone and extremities.