Deep Learning-Based Ultrasound Imaging Diagnosis for Gonadotropin-Releasing Hormone Agonists Treatment of Central Precocious Puberty
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.