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

Artificial intelligence (AI) systems have been developed to estimate gestational age (GA) based on blind sweep ultrasonography scans performed by novice operators. However, their generalizability to new clinical contexts outside original training settings requires evaluation.

To investigate the ability of a previously trained AI system to interpret blind sweep ultrasonography scans for GA estimation to generalize to new clinical environments.

This prospective diagnostic study compares AI model performance against the clinical standard for GA determination. The model was primarily accessed following lightweight adaptation using data from 1 setting. From a broader cohort of 2043 participants (1008 in Chicago and 1035 in Nairobi), datasets included an adaptation set of 120 Chicago participants (split evenly for training and validation) and a primary evaluation set of 385 participants (192 in Chicago and 193 in Nairobi) with GAs of 16 to 36 weeks. The study took place in 2 urban medical institutions, 1 in Chicago, Illinois (March 7, 2022, to May 28, 2024) and 1 in Nairobi, Kenya (January 8 to September 2, 2024). Data were analyzed April 21 to June 4, 2025.

A mobile application containing the AI system for GA estimation, with data collected via a protocolized blind sweep procedure by novice operators.

The primary outcome was the mean absolute error (MAE) of the fine-tuned AI's GA estimation compared with the clinical standard (expert sonographer estimate), using a 1-day noninferiority margin.

The primary evaluation set included 385 participants (mean [SD] maternal age, 31.3 [5.6] years). The adapted model achieved an MAE of 4.1 days (95% CI, 3.5-4.7) in Chicago and 4.3 days (95% CI, 3.8-4.8 days) in Nairobi. Overall, the AI model demonstrated noninferiority to clinical standard estimates (MAE, 4.2 days [95% CI, 3.8-4.6 days] vs 4.5 days [95% CI, 4.1-4.9 days]; P < .001).

These findings suggest that the AI model for GA estimation, using blind sweep data collected by novice operators, demonstrated generalization to new clinical settings and a different ultrasonography probe. This study highlighted the potential of AI-based ultrasonography assessment to expand access to care through scalable cross-setting deployment, particularly in low-resource settings.