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Updated: Aug 13, 2026
State of the Art Cranial Ultrasound Imaging in Neonates
Published on: February 2, 2015
Artificial intelligence model based on the detection of key anatomical structures for identifying standard planes on
Qingyu Zheng1, Xiaoqian Zhang1, Liya Li1
1Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Background:
Prenatal ultrasound examination at 11+0 to 13+6 weeks' gestation can detect approximately 33-50% of major fetal structural abnormalities. Despite recent advances in artificial intelligence (AI) for fetal ultrasound, its application in examining the first-trimester fetal central nervous system (FCNS) remains limited. This study aimed to develop, validate, and determine the clinical value of an AI FCNS model (FCNS-Model) based on the detection of key anatomical structures with the purpose of identifying FCNS ultrasound standard planes (FCNS-USPs).
Methods:
This study included 2,901 FCNS-USPs images from three hospitals, which were split into a training set (2,124 images), a test set (264 images), an internal validation set (254 images), and a clinical validation set (259 images). Additionally, an independent external validation set (300 images) was formed from images acquired at a fourth hospital. The performance of junior, intermediate, and senior physicians, as well as that of the FCNS-Model, was compared against the standard of an expert ultrasound team.
Results:
In the test set, the model achieved precision, recall, and F1 scores above 92%. For the internal validation set, classification accuracy for each plane was 99.3-100%, with kappa values >0.90, indicating strong agreement with the expert team. In the clinical validation set, FCNS-Model achieved significantly higher areas under the curve (AUCs) than did the senior physician for the anterior-posterior midsagittal plane (0.984 vs. 0.906; P<0.001), the transverse plane through the thalamus (0.982 vs. 0.938; P=0.003), and the coronal plane through the frontal lobe (0.915 vs. 0.852; P=0.03). For the transverse plane of the lateral ventricle, the FCNS-Model's AUC of 0.979 was slightly higher than that of the junior physician (AUC =0.971; P=0.042). Furthermore, McNemar testing demonstrated that the FCNS-Model achieved significantly higher sensitivity than did the junior physician in identifying FCNS-USPs (P=0.02). In the external validation set, the model maintained stable performance despite a moderate decrease, supporting its generalizability.
Conclusions:
FCNS-Model accurately identifies FCNS-USPs and is an effective tool for assisting ultrasound physicians in obtaining standard planes and identifying key anatomical structures, particularly in settings with varying levels of operator experience.
