Artificial Intelligence Algorithm-Based Analysis of Ultrasonic Imaging Features for Diagnosis of Pregnancy Complicated with Brain Tumor

Abstract

This research was to explore the accuracy of ultrasonic diagnosis based on artificial intelligence algorithm in the diagnosis of pregnancy complicated with brain tumors. In this study, 18 patients with pregnancy complicated with brain tumor confirmed by pathology were selected as the research object. Ultrasound contrast based on artificial bee colony algorithm was performed and diagnosed by experienced clinicians. Ultrasonic image will be reconstructed by artificial bee colony algorithm to improve its image display ability. The pathological diagnosis will be handed over to the physiological pathology laboratory of the hospital for diagnosis. The doctor’s ultrasonic diagnosis results were compared with the pathological diagnosis stage results of patients, and the results were analyzed by statistical analysis to evaluate its diagnostic value. The comparison results showed that the number and classification of benign tumors were the same, while in malignant tumors, the number diagnosis was the same, but there was one patient with diagnostic error in classification. One case of mixed glial neuron tumor was diagnosed as glial neuron tumor, and the diagnostic accuracy was 94.44% and the K value was 0.988. The diagnostic results of the two were in excellent agreement. The results show that, in the ultrasonic image diagnosis of patients with brain tumors during pregnancy based on artificial intelligence algorithm, most of them are benign and have obvious symptoms. Ultrasound has a good diagnostic accuracy and can be popularized in clinical diagnosis. The results can provide experimental data for the clinical application of ultrasonic image feature analysis based on artificial intelligence as the diagnosis of pregnancy complicated with brain tumors.

Ultrasound of Fetal Cardiac Function Changes in Pregnancy-Induced Hypertension Syndrome

“…Furthermore, the hypertension group had significantly lower neonatal weights versus the normal group, which may be attributable to the constriction and narrowing of the spiral artery and the target vessel of the placenta, resulting in the reduction of blood flow in the small spiral arteries of the meconium layer, ischemia, and hypoxia in the intervillous space and small vessels of the meconium, thereby compromising the growth and development of the fetus and causing fetal weight loss [ 13 , 14 ]. Sound maternal health care, proper diet, and maintenance of a positive psychological state can prevent the development of PIH in pregnant women to achieve timely and accurate judgment, prevention, and aggressive treatment, which are considered effective in reducing maternal and infant morbidity and mortality [ 15 ].…”

The Integration of Artificial Intelligence into Clinical Practice

“…In certain medical applications such as obstetric pelvic and echocardiography ultrasonography, where visual analysis and measurement play a crucial role, the utilization of video clips can provide a comprehensive set of structured data. This enables spatiotemporal analysis and enhances the advantages of ANNs [125]. A study investigated the efficacy of AI algorithms in ultrasonic diagnosis for pregnant patients with brain tumors.…”

A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology

“…Hence, the AI application in ultrasonography during pregnancy could support physicians in triaging and diagnosing the body of pregnant women. For instance, in obstetric pelvic and echocardiography ultrasonography, wherein visual analysis and measurement are essential, the video clips would offer the entire group of relevant structured data, permitting spatiotemporal analysis and increasing the benefits of ANNs [15]. [16].…”