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A study conducted at Peking Union Medical College Hospital in Beijing, China, has developed a deep learning method for detecting and segmenting facet joints in ultrasound images. The researchers used convolutional neural networks (CNNs) and enhanced data annotation to improve the accuracy of the model. The study included 300 patients undergoing pain treatment, and the deep learning model achieved a 90.4% detection rate and an 85.0% segmentation rate for facet joints. This research demonstrates the potential of deep learning techniques in analyzing ultrasound images of facet joints. [Extracted from the article]

A recent study conducted by researchers at Capital Medical University in Beijing, China aimed to establish reference intervals for normal fetal lung biological parameters in the Chinese population. The study included 1388 normal single pregnant women at 21-40 weeks of gestation and used image analysis software to measure various parameters related to fetal lung development. The results showed that the left and right lung areas and lung area to head circumference ratio (LHR) positively correlated with gestational age. The study concluded that these reference intervals can be used for the prenatal non-invasive assessment of fetal pulmonary hypoplasia in Chinese populations. [Extracted from the article]