Abstract
Background: Real-time shear wave elastography (SWE) has emerged as a useful imaging modality for detecting the lesion location of various diseases, including cervical diseases.
Objectives: In this paper, the SWE was used to quantitatively determine the tissue hardness of the internal and external orifice of the cervix (IOC & EOC) and to relatively objectively analyze the impact of different production methods on the hardness of the cervical tissue.
Methods: A total of 48 patients were selected, and they were divided into three groups according to different production methods: control group (16 cases), cesarean section group (16 cases), and spontaneous delivery group (16 cases). Artificial intelligence has also been incorporated into this work. A deep flexible neural tree model and a new set of FNT models were proposed to assist in classifying cervical physical data in different states. The physical data was extracted as the features, and the different states were considered as category labels.
Results: There was no statistically significant difference in the elasticity of the IOC and the EOC between the groups. However, the difference in the elasticity of the IOC and the EOC within each group was statistically significant. The classification results corresponded with the results of the statistical analysis. The hardness of the EOC is generally lower than that of the IOC, and there was no significant difference in hardness between the IOC and the EOC in the three groups.
Conclusion: There is no significant difference in the cervical elasticity hardness between different delivery modes.
Keywords: Shear wave, Quantitative analysis, Cervix, Cesarean section, Spontaneous delivery, Delivery modes.
Call for Papers in Thematic Issues
Advancements and Applications of Photon Counting CT: Transforming Imaging Precision and Quantification
Photon Counting Computed Tomography (CT) represents a transformative advancement in medical imaging technology, offering unprecedented levels of precision and diagnostic capability. This special issue will explore the latest innovations and applications of photon counting CT, highlighting its advantages over traditional energy-integrating detectors. The scope includes an in-depth examination of its ...read more
Advancements in Deep Learning based Medical Image and Signal Processing for Healthcare Applications
The accurate predication from medical data (signals and images) analytics will be achieved through the use of cutting-edge AI tools for tailoring medical treatment to each patient. When the agents are done analysing the user's health data, the user can consult with the agents to receive advice and the most ...read more
AI-Driven Diagnostics in Radiology and Medical Imaging
Medical imaging data, such as X-rays, MRIs, ultrasounds, CT scans, and DXAs, can be analysed by AI algorithms to help healthcare professionals identify and diagnose diseases more rapidly and accurately. Among the most cutting-edge applications of artificial intelligence (AI) in healthcare are imaging-related applications like cancer detection. AI could revolutionise ...read more
Applying medical image processing techniques for early cancer detection in mammograms
Breast cancer survival and successful treatment are significantly boosted by early identification. Breast cancer screening is most commonly accomplished with mammography. The radiologist's experience and the image quality, among several other factors, might affect how well a mammogram is interpreted. Enhancing mammography analysis and increasing cancer early detection are made ...read more
12
