Fetal Weight Estimation Using Automated Fractional Limb Volume With 2-Dimensional Size Parameters: A Multicenter...

Area of Science:

• Maternal-Fetal Medicine
• Medical Imaging
• Biometry

Background:

• Accurate fetal weight prediction is crucial for optimal perinatal management.
• Existing fetal weight estimation models have limitations in accuracy across different fetal sizes.
• Automated fractional limb volume (FLV) offers a novel approach to fetal biometry.

Purpose of the Study:

• To develop and validate new fetal weight prediction models utilizing automated fractional limb volume (FLV).
• To compare the accuracy of FLV-based models against established fetal weight estimation methods.

Main Methods:

• A prospective multicenter study involving 328 pregnancies.
• Three-dimensional ultrasound data acquisition, including automated FLV derived from humerus (AVol) or femur (TVol) diaphysis.
• Development of four weight estimation models using population sample-specific regression coefficients and comparison with actual birth weights (BWs).

Main Results:

• FLV-based models (AVol and TVol) improved the percentage of correctly classified birth weights (±10%) to 83.2% and 83.9%, respectively, outperforming the INTERGROWTH-21st model (73.8%).
• Sample-specific coefficients minimized bias, achieving high accuracy (e.g., AVol: 0.3% ± 7.4% difference from actual BW).
• Models showed good performance for both low (<2500g) and high (>4000g) birth weights, with AVol and TVol models showing improved classification rates for larger fetuses.

Conclusions:

• Automated fractional limb volume (FLV) measurements, when integrated with conventional 2D biometry, generally enhance fetal weight prediction accuracy.
• The developed FLV-based models demonstrate potential for more precise fetal weight estimation in clinical settings.