Artificial Intelligence (AI)-based tools are revolutionizing clinical practice, especially in obstetrics and perinatology, where they aid in fetal diagnostics using ultrasonographs. One such AI tool is SonoCNS™, designed to automatically measure fetal head structures like biparietal diameter (BPD) and head circumference (HC). This study aimed to assess the repeatability and reproducibility of SonoCNS™ measurements in the second and third trimesters of pregnancy. The research included 381 patients undergoing manual and automated biometric measurements, revealing good to excellent reproducibility for BPD, HC, and OFD, but poorer performance for CM and Vp measurements.
The study highlighted that while SonoCNS™ offers reliable biometric measurements, it showed limitations in measuring intracranial structures like CM and Vp. The software’s ability to automatically delineate planes and reduce examination time was noted as a significant advantage, especially for novice sonographers. However, challenges were observed in cases where the system failed to measure specific values due to unclear boundaries or difficult examination conditions. The study emphasized the importance of precision in certain measurements crucial for estimating fetal weight.
Comparisons with previous studies underscored the variability in results, with different protocols and patient demographics influencing outcomes. While SonoCNS™ demonstrated good performance in estimating fetal skull biometry, improvements are needed for measuring intracranial structures. The study’s limitations included factors like subcutaneous fat thickness and fetal positioning not being considered, highlighting the need for further research in diverse populations and conditions.
Looking ahead, AI-based systems like SonoCNS™ hold promise for enhancing fetal screening studies, with the potential to classify abnormal scans and estimate gestational age accurately. The future of prenatal ultrasound diagnosis lies in advanced AI algorithms capable of real-time image analysis and anomaly detection, paving the way for efficient and accurate fetal diagnostics.
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