Typical Morphological Features on Prenatal Ultrasound of Fetuses With Trisomy 13 (Patau’s Syndrome)

Tran, Danh Cuong; Dang, Anh Linh; Nguyen, Thi Bich Van; Anh, Tran Van; Nguyen, Thi Hue; Le, Thi Minh Phuong; Tran, Linh T.; Ngo, Toan Anh; Hoang, Thi Ngoc Lan; Le, Phuong Thao; Ngo, Thi Tuyet Nhung; Le, Thi Quyen; Nguyen, Thi Trang

doi:10.14740/jcgo848

Cited by 1 publication

(1 citation statement)

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“…In clinical engineering, US imaging has developed as a strong technique for visualising and monitoring fetal development [16]. The field of US imaging has made contributions to the detection and knowledge of prenatal anomalies [17][18][19]. It is critical to thoroughly understand the fetal anatomy and organ development in order to use ultrasonography efficiently for this purpose [20].…”

Section: Fetal Anatomymentioning

confidence: 99%

Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review

Sriraam,

Chinta,

Suresh

et al. 2024

Prog. Biomed. Eng.

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For prenatal screening, ultrasound imaging allows for real-time observation of developing fetal anatomy. Understanding normal and aberrant forms through extensive fetal structural assessment enables for early detection and intervention. However, the reliability of anomaly diagnosis varies depending on operator expertise and device limits. First trimester scans in conjunction with circulating biochemical markers are critical in identifying high-risk pregnancies, but they also pose technical challenges. Recent engineering advancements in automated diagnosis, such as AI-based ultrasound image processing and multimodal data fusion, are developing to improve screening efficiency, accuracy, and consistency. Still, creating trust in these data-driven solutions is necessary for integration and acceptability in clinical settings. Transparency can be promoted by explainable AI (XAI) technologies that provide visual interpretations and illustrate the underlying diagnostic decision making process. An explanatory framework based on deeplearning is suggested to construct charts depicting anomaly screening results from ultrasound video feeds. AI modeling can then be applied to these charts to connect defects with probable deformations. Overall, engineering approaches that increaseimaging, automation, and interpretability hold enormous promise for altering traditional workflows and expanding diagnostic capabilities for better prenatal care.

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