Evaluation of Endocrine and Metabolic Changes in Polycystic Ovary Syndrome by Ultrasonic Imaging Features under an Intelligent Algorithm.

Abstract

To explore the application of intelligent algorithm-based ultrasound in the evaluation of polycystic ovary syndrome (PCOS) and the endocrine and metabolic changes of PCOS, 44 patients diagnosed with PCOS were recruited and rolled into three groups regarding detection methods. Backpropagation algorithm-based ultrasonic detection was adopted for the patients in the experimental group. The patients in the control group were tested by conventional ultrasound. In addition, 18 healthy volunteers were selected as the normal group. The results showed that the images processed by the backpropagation algorithm were substantially better than the traditional ultrasound images (P < 0.05), and the image display was clearer. S, A, and S/A ratios measured by two different detection methods were 7.8 mm2, 3.5 mm2, and 0.449 in the experimental group, respectively, which were significantly different from 6.3 mm2, 2.6 mm2, and 0.413 in the control group (P < 0.05). The PI and RI values of the interstitial ovarian artery in the experimental group were lower than those in the control group, and the systolic peak velocity (PSV) and end diastolic velocity (EDV) values were higher than those in the control group (P < 0.05). Compared with the control group, the ovarian volume, interstitial vascularization-flow index (VFI), and flow index (FI) in the experimental group were substantially increased, and the total number of detected follicles was more (P < 0.05). The level of follicle-stimulating hormone (FSH) in PCOS patients was substantially lower than that in normal controls (P < 0.05). The LH, E2, P, and T of PCOS patients were substantially higher than those of normal controls (P < 0.05). Ultrasound on account of the backpropagation algorithm can directly display the three-dimensional structure of the ovary and follicle and accurately measure the ovarian volume and follicle number. Endocrine and metabolic indicators can provide objective information for the clinical diagnosis of PCOS and can be used as a way of clinical evaluation of PCOS.