Deep Learning-Based Segmentation of Fetal Anatomical Structures in the First Trimester
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The authors aimed to develop and evaluate an AI system for the automatic identification and classification of fetal anatomical structures during the first trimester using ultrasound images. Their results showed that the YOLACT model achieved a high anatomical detection accuracy of 98.4% and demonstrated effective real-time processing capabilities, indicating its potential for clinical use in early anomaly screening.
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