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AI May Estimate Gestational Age
An adapted AI model estimated gestational age with accuracy comparable to the clinical standard when novice operators performed blind sweep ultrasonography in two clinical settings.
To evaluate the accuracy of an AI model in estimating gestational age from blind sweep ultrasonography in new clinical settings.
Approach:
Study Design: A prospective multicenter diagnostic study was conducted with 2,043 patients enrolled at urban medical centers in Chicago and Nairobi.
Participants: 385 patients with pregnancies between 16 and 36 weeks were included in the primary analysis.
Methods: Novice operators performed blind sweep ultrasonography using Clarius probes, and AI estimates were compared to standard clinical ultrasonography by expert sonographers.
Primary Outcome: The mean absolute error (MAE) of gestational age estimation compared to the clinical standard.
Key Findings:
The AI model achieved an MAE of 4.2 days, compared to 4.5 days for the clinical standard.
Performance was consistent across both sites, with MAEs of 4.1 days in Chicago and 4.3 days in Nairobi.
No systematic bias was observed in gestational age estimation by the AI model.
Performance remained stable across different reference dating windows.
The AI model showed improved accuracy for fetuses above the 90th weight percentile.
Interpretation:
The AI model demonstrated noninferior performance to the clinical standard in estimating gestational age across different clinical settings.
Limitations:
The model was adapted using data only from Chicago prior to external validation.
Generalizability was evaluated using a single new ultrasound manufacturer.
Differences in operator training between sites may have influenced results.
Subgroup analyses were based on relatively small sample sizes.
Investigators found that adding pregnancy-related and female-specific factors modestly improved postpartum cardiovascular risk prediction compared with traditional risk factors alone.
