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This article discusses a study that aims to develop an artificial intelligence (AI) algorithm for the automatic classification of ultrasound images of the fetal brain in the first trimester. The algorithm focuses on identifying abnormal sonographic findings of the posterior cranial fossa (PCF), which can be early markers of open spina bifida or Dandy Walker malformation. The study used a dataset of 251 images and achieved promising results, demonstrating the potential for the AI algorithm to support clinicians in detecting major central nervous system anomalies during early pregnancy. The next phase of the study will assess the algorithm's clinical applicability in routine practice. [Extracted from the article]

This article analyzes the accuracy of first trimester referral ultrasounds compared to second trimester referral ultrasounds in detecting false positives and false negatives for structural anomalies in singleton pregnancies. The study found that the rate of false positive and false negative diagnoses in the first trimester ultrasounds was low. False negatives were mostly minor structural anomalies with little impact on management and counseling, or evolving anomalies that cannot be diagnosed in the first trimester. False positives were consistent with the natural history of the anomaly, although over-diagnosis was acknowledged as a limitation of first trimester referral ultrasounds. [Extracted from the article]