Abstract
To explore the adoption of ultrasound imaging diagnosis based on deep learning of convolutional neural networks (CNNs) in the treatment of central precocious puberty (CPP) by gonadotropin-releasing hormone agonists (GnRHa), ultrasound imaging based on CNN was utilized to treat CPP. The bone age, uterine and ovarian volume, and breast development of incomplete precocious puberty (IPP) group and CPP group were observed and recorded. The peak values of luteinizing hormone (LH) and follicle-stimulating hormone (FSH) were measured. The uterine and ovarian volume before and after GnRHa treatment of CPP were compared. The results showed that the bone age (9.03 ± 1.07), uterine volume (2.37 ± 1.52), ovarian volume (2.36 ± 0.82 mL), and breast development of the CPP group were considerably higher in contrast to the IPP group and control group ( P < 0.05 ). The LH peak (11.97 ± 5.63) and FSH peak (12.89 ± 3.15) of the CPP group were substantially higher relative to the IPP group ( P < 0.05 ). The uterine volume (1.06 ± 0.42) and ovarian volume (1.12 ± 0.49) after treatment were inferior to those before treatment ( P < 0.05 ). In short, ultrasound images based on deep learning could diagnose precocious puberty, which could also provide a certain basis for GnRHa treatment of CPP, as well as an important basis for clinical diagnosis and treatment of precocious puberty.
Highlights
With the rapid development of social economy, food safety issues, environmental issues, family education issues, and mental and psychological issues have gradually become prominent, which begin to affect the physical and mental development of children, and the incidence of precocious puberty has increased notably in recent years [1]
Bone age Age bone age of the central precocious puberty (CPP) group was notably greater in contrast to the incomplete precocious puberty (IPP) group and the controls, and the difference was very considerable (P < 0.05). ere was no remarkable difference in bone age between the IPP group and the controls (P > 0.05)
A standard measurement of the transverse diameter × the upper and lower diameter × the front and rear diameter of the uterus and ovary of the girl was done. en, the volume of the uterus and ovaries was calculated. e results showed that the uterine volume of girls in the CPP group was 2.37 ± 1.52 measured. Uterine volume (mL), and the uterine volume of girls in the IPP group was 1.32 ± 1.03 mL. e uterine volume of normal girls in the control group was 0.81 ± 0.34 mL
Summary
With the rapid development of social economy, food safety issues, environmental issues, family education issues, and mental and psychological issues have gradually become prominent, which begin to affect the physical and mental development of children, and the incidence of precocious puberty has increased notably in recent years [1]. Precocious puberty is an abnormality in the time of puberty, which refers to secondary sexual characteristics and internal and external genital development before the age of 8 in girls and 9 in boys [2, 3]. E most common type of precocious puberty in girls is CPP [5]. CPP girls are too early at menarche, which may lead to psychological and behavioral abnormalities, and may cause social psychological behavior problems. It can be used to identify some distorted and undeformed two-dimensional graphics such as displacement and zoom. It is one of the representative algorithms of deep learning. Neural network has a huge number of connections, making it easy to introduce spatial information, and can better solve the problems of unevenness and noise in image recognition. e first CNN is a time delay
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