Generalization of AI-Based Gestational Age Assessment Using Blind Sweep Ultrasonography - Report - MDSpire

Generalization of AI-Based Gestational Age Assessment Using Blind Sweep Ultrasonography

  • By

  • Angelica Willis

  • Chace Lee

  • Justin Krogue

  • Anneka Wickramanayake

  • Nichole Young-Lin

  • Stacey Caron

  • Priscah Cheruiyot

  • Amber Watters

  • Alicia Martín

  • Tiya Tiyasirichokchai

  • Akib Uddin

  • Yun Liu

  • Patricia Strachan

  • Sunny Jansen

  • Alyssa Santi

  • Preeti Singh

  • Catherine Arguelles

  • Kenneth Hudson

  • David Melnick

  • Mahesh Vaidyanathan

  • Mozziyar Etemadi

  • Yossi Matias

  • Avinatan Hassidim

  • Greg S. Corrado

  • Shruthi Prabhakara

  • Daniel Golden

  • Ryan G. Gomes

  • Shravya Shetty

  • July 9, 2026

  • 0 min

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Clinical Report: Expansion of AI-Driven Gestational Age Evaluation

Overview

This study evaluates the generalizability and performance of AI models for gestational age estimation using blind sweep ultrasonography across diverse clinical settings.

Background

Access to prenatal ultrasonography is critical for monitoring fetal development and detecting complications. Traditional ultrasound methods require skilled operators and expensive equipment, limiting their availability, especially in low-resource settings. AI-driven ultrasound techniques, particularly blind sweep ultrasonography, offer a potential solution to enhance maternal health outcomes.

Data Highlights

This study included 385 participants with gestational ages between 16 to 36 weeks, evaluated across two clinical sites in Chicago and Nairobi. The AI model's performance was compared to standard procedure estimates derived from traditional ultrasound methods.

Key Findings

  • AI models demonstrated effective generalization to new clinical settings with novice operators.
  • Gestational age estimation accuracy is crucial for managing obstetric care and preventing complications.
  • Participants were recruited based on strict eligibility criteria, ensuring a diverse representation of gestational ages.
  • Standard-of-care ultrasound was performed within one week of the blind sweep to validate AI estimates.
  • The study adhered to STARD reporting guidelines, enhancing the reliability of its findings.

Clinical Implications

The use of AI in gestational age estimation can facilitate timely obstetric interventions.

Conclusion

The study supports the potential of AI-driven blind sweep ultrasonography to enhance gestational age estimation across varied clinical environments.

Related Resources & Content

  1. npj Digital Medicine, 2025 -- Fetal gestational age estimation using artificial intelligence on non-targeted ultrasound images and video
  2. BMC Pregnancy and Childbirth, 2026 -- Artificial intelligence-assisted quality assessment of mid-trimester ultrasound examinations using large vision-language models
  3. Frontiers in Digital Health, 2026 -- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation
  4. Methods for Estimating the Due Date | ACOG, 2025
  5. Journal of Medical Internet Research (JMIR) — Adaptive Fast-Slow Large Language Model Framework for Multidimensional Classification of Prenatal Ultrasound Reports: Comparative Study
  6. WHO antenatal care recommendations for a positive pregnancy experience
  7. Methods for Estimating the Due Date | ACOG
  8. Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps - PMC
  9. Fetal gestational age estimation using artificial intelligence on non-targeted ultrasound images and video | npj Digital Medicine
  10. Target product profile for obstetric ultrasound devices

Original Source(s)

Generalization of AI-Based Gestational Age Assessment Using Blind Sweep Ultrasonography

  • by Angelica Willis, Chace Lee, Justin Krogue, Anneka Wickramanayake, Nichole Young-Lin, Stacey Caron, Priscah Cheruiyot, Amber Watters, Alicia Martín, Tiya Tiyasirichokchai, Akib Uddin, Yun Liu, Patricia Strachan, Sunny Jansen, Alyssa Santi, Preeti Singh, Catherine Arguelles, Kenneth Hudson, David Melnick, Mahesh Vaidyanathan, Mozziyar Etemadi, Yossi Matias, Avinatan Hassidim, Greg S. Corrado, Shruthi Prabhakara, Daniel Golden, Ryan G. Gomes, Shravya Shetty

  • July 9, 2026

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