A global survey has revealed that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses are typically made after birth. Facial deformities are commonly associated with chromosomal disorders. Prenatal diagnosis through ultrasound imaging is vital for identifying abnormal fetal facial features. However, this approach faces challenges such as inconsistent diagnostic criteria and limited coverage. To address this gap, we have developed FGDS, a three-stage model that utilizes fetal ultrasound images to detect genetic disorders. Our model was trained on a dataset of 2554 images. Specifically, FGDS employs object detection technology to extract key regions and integrates disease information from each region through ensemble learning. Experimental results demonstrate that FGDS accurately recognizes the anatomical structure of the fetal face, achieving an average precision of 0.988 across all classes. In the internal test set, FGDS achieves a sensitivity of 0.753 and a specificity of 0.889. Moreover, in the external test set, FGDS outperforms mainstream deep learning models with a sensitivity of 0.768 and a specificity of 0.837. This study highlights the potential of our proposed three-stage ensemble learning model for screening fetal genetic disorders. It showcases the model’s ability to enhance detection rates in clinical practice and alleviate the burden on medical professionals.

The accurate, quantitative, and objective prediction of the brain age for premature infants will contribute to the exploration of brain maturity and catch-up growth. Traditional approaches rely heavily on a pediatrician’s clinical experience, which makes the whole process time-consuming and labor-intensive. To solve this problem, we propose a deep learning-based brain age prediction model for preterm infants via neonatal MRI for this purpose, and it is called as BAPNET for short. First of all, we collected a specific dataset including MR images of 281 preterm infants. Then, a pretraining model (DeepBrainNet) is applied as the main backbone, and transfer learning is utilized to enhance the baseline model by making knowledge transfer from the ImageNet dataset. The proposal can be viewed as a specific prediction model by absorbing knowledge enhancement from peripheral visual features. On a test set of 70 preterm infants held out from the original dataset, 2D-BPANET achieved results with an mean square error (MAE) of 1.15 and the 95% - 95% content tolerance interval for a difference (prediction and ground truth) of [-3.82, 3.39], whereas 3D-BPANET achieved better results with an MAE of 1.8 and a difference of [0.51, 3.09]. Meanwhile, we leverage heatmaps to verify the consistency between hindbrain regions and cortical fold regions outputted by our model and the latest studies of brain development in preterm infants. In conclusion, BPANET demonstrates that deep learning can estimate brain maturity in preterm infants and provides a reference standard for preterm infant brain development, which could be applied as a promising tool.

Congenital heart disease (CHD) is the most common major congenital anomaly and a leading cause of perinatal mortality and long-term neurodevelopmental disa