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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Deep Learning Model for Real-Time Nuchal Translucency Assessment at Prenatal US.

Yuanji Zhang1,2,3, Xin Yang1,2,3, Chunya Ji4

  • 1National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, No. 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, PR China.

Radiology. Artificial Intelligence
|April 30, 2025
PubMed
Summary
This summary is machine-generated.

An artificial intelligence model accurately identifies the nuchal translucency (NT) plane and measures NT thickness in prenatal ultrasound assessments, showing consistency with radiologist workflow.

Keywords:
Convolutional Neural Network (CNN)DiagnosisFeature DetectionFetusSegmentationUltrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Prenatal Diagnostics

Background:

  • Nuchal translucency (NT) measurement is crucial for early screening of fetal aneuploidies.
  • Accurate and efficient NT plane identification and measurement in prenatal ultrasound (US) are essential for reliable screening.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence (AI)-based model for real-time NT plane identification and measurement.
  • To assess the AI model's accuracy and consistency compared to experienced radiologists.

Main Methods:

  • A retrospective multicenter study developed and evaluated the automated identification and measurement of NT (AIM-NT) model.
  • The AIM-NT model's performance was compared against radiologists on internal (3959 images) and external (267 videos) datasets.
  • Discrepancies in NT plane identification and thickness measurements were analyzed.

Main Results:

  • AIM-NT achieved an area under the receiver operating characteristic curve of 0.92 for NT plane identification on internal data.
  • No significant differences were found between AIM-NT and radiologists in NT plane identification accuracy (88.8% vs 87.6%) or NT thickness measurements on external data.
  • High consistency was observed in NT plane identification time and thickness measurements (mean difference 0.03 mm).

Conclusions:

  • The AIM-NT model demonstrates high accuracy in identifying the NT plane and measuring NT thickness on prenatal US.
  • The AI model shows minimal discrepancies with radiologist workflow, suggesting potential for integration into clinical practice.