Clinical Analysis of Improved Particle Swarm Algorithm-Based Magnetic Resonance Imaging Diagnosis of Placenta Accreta

• Published in: Contrast media and molecular imaging Vol. 2021; pp. 1 - 6
• Main Authors: Ding, Xiaoyan, Cao, Yingying, Sun, Fengtao, Ma, Airong, Zhang, Feiyue
• Format: Journal Article
• Language: English
• Published: England Hindawi 13.08.2021
• Subjects: Adult; Algorithms; Cesarean Section; Female; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Middle Aged; Placenta - diagnostic imaging; Placenta - pathology; Placenta Accreta - diagnosis; Placenta Accreta - diagnostic imaging; Placenta Accreta - pathology; Pregnancy; Ultrasonography, Prenatal - methods; Young Adult
• ISSN: 1555-4309; 1555-4317; 1555-4317
• DOI: 10.1155/2021/7373637

The magnetic resonance imaging (MRI) image processing capabilities were investigated based on the improved particle swarm optimization (IPSO) algorithm, and the clinical application analysis of MRI images in the diagnosis of placenta accreta (PA) was evaluated in this study. The MRI uterine images were detected on the basis of IPSO. Besides, the clinical data of 89 patients with PA were selected and collected, who were diagnosed by clinical cesarean section surgery and pathological comprehensive diagnosis in hospital from January 2018 to July 2020. Then, all of them underwent the ultrasound (US) and MRI examinations, and the differences of sensitivity, specificity, and accuracy between MRI and US under IPSO in the diagnosis of PA were compared, as well as the differences in the diagnosis of adhesive, implantable, and penetrated PA. The results showed that the difference in detection between IPSO-based MRI images and US images was not statistically substantial (p>0.05), but the number of initial detections was higher than the number of US examination. MRI examination had higher sensitivity and specificity in the diagnosis of PA during pregnancy, especially for implantable PA, compared with US examination (p<0.05). In conclusion, MRI images based on the improved particle swarm optimization algorithm showed a good application effect in the diagnosis of placental implantation diseases, which was worthy of further promotion in clinical practice.