Objectives: To report the Cesarean section rate in Al-
Batool Maternity Teaching Hospital and to identify
how many of them were done for maternal and fetal
condition.
Design: A statistical cross sectional study.
Setting: The study was conducted at Al-Batool
Maternity Teaching Hospital (13000 deliveries per
year).
Participants: The patients group consists of 4556
patients admitted for delivery (vaginal and abdominal)
during a period of four months commencing from
January 2003.
Main outcome measures: calculation of all live
births, calculation of cesarean section rate, percentage
of the primary cesarean sections and the repeat
cesarean sections and listing the indications of the
operation according to maternal and fetal condition
with their percentage.
Results: Total births during the period of this study
were 4556 births, 3732 vaginal deliveries and 824
cesarean sections. Cesarean section rate was found to
be 17.94% of total live births, the most frequent
indication for cesarean section was malpresentation
(24.3%). Primary cesarean sections contribute to 75%
of cases.
Conclusions: A primary cesarean section is one of the
most important causes of high cesarean section rate.
Decreasing the incidence of primary operations will
help in reducing cesarean section rate.
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The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of
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The growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to co
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The deterioration of buried sewers during their lifetime can be affected by several factors leading to bad performance and can damage the infrastructure similar to other engineering structures. The Hydraulic deterioration of the buried sewers caused by sewer blockages while the structural deterioration caused by sewer collapses due to sewer specifications and the surrounding soil characteristics and the groundwater level. The main objective of this research is to develop deterioration models, which are used to predict changes in sewer condition that can provide assessment tools for determining the serviceability of sewer networks in Baghdad city. Two deterioration models were developed and tested using statistical software SPSS, the
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