Abstract:

Objective

To address the issue of strong reliance on experience in identifying abnormal features during prenatal ultrasound diagnosis of fetal omphalocele， an intelligent auxiliary quality control and diagnosis model was developed to enhance the screening efficiency of primary physicians.

Methods

The data of pregnant women who underwent prenatal ultrasound examinations at Shenzhen Maternal and Child Health Hospital from January 2016 to December 2024 were retrospectively collected.A total of 324 cases of fetal omphalocele （1620 images） and 1000 cases of normal fetuses （2555 images）were included.After being labeled by ultrasound experts， the data were divided into training set， validation set， and test set at a ratio of 7：2：1 based on the case level.A dual-task model based on YOLO V8 was constructed： assessment of the quality of the cross-sectional images of the umbilical cord abdominal wall insertion （standard/non-standard） and detection of abnormal abdominal wall structures （abdominal wall defect， protruding mass）.The efficacy of the model in detecting target structures was evaluated by precision，recall rate， and average accuracy.The consistency and agreement between the model's section classification and the true category were analyzed by accuracy and weighted Kappa coefficient.

Results

The average accuracy of the model for detecting key structures reached 0.932.The detection of abdominal wall contour（0.987）， umbilical cord insertion （0.977）， and protruding mass （0.938） was excellent， while the detection accuracy of abdominal wall defect was slightly inferior （0.826）.The classification accuracy of the model for standard sections， non-standard sections， and omphalocele was 96.8% （151/156）， 100% （55/55）， and 96.1%（148/154）， respectively， and was highly consistent with the true category （weighted Kappa = 0.955， P＜0.001）.

Conclusion

The artificial intelligence model based on YOLO V8 can achieve standardized quality control and abnormal feature identification in prenatal ultrasound diagnosis of omphalocele， and is particularly suitable for assisting primary physicians in rapid prenatal ultrasound screening.

Key words: Fetal omphalocele, AI-powered ultrasound diagnosis, Quality control, Artificial intelligence