Background: The incidence of maternal mortality in
placenta previa accrete is 7%,and its preoperative
diagnosis is of a great value.
Objective: to evaluate the efficacy of transabdominal
color Doppler ultrasound in diagnosing placenta
previa accreta and inccreta. Color Doppler imaging
criteria used in: includes diffuse parenchymal
placental lacunar flow, focal intra parenchymal
placental lacunar flow and bladder uterine serosa
interphase hyper-vascularity.
Design: Prospective study on patients from
January2007 to January 2008.
Patients and method: 48patients with one caesarean
section or more and with persistent anterior placenta
previa diagnosed by transabdominal ultrasound were
examined by color Doppler at 32 or more weeks of
gestation to determine the possibility of myometrial
invasion depending on the above color Doppler
criteria.
Results: Out of 48 patients eleven exhibited
characteristic Doppler imaging pattern highly specific
for placenta accreta and inccreta according to
preceding criteria , one patient had false positive color
Doppler imaging result, 5 patients underwent
caesarian hysterectomy , the remaining five were
treated conservatively by uterine artery ligation and
other conservative measures because of bleeding
from the lower uterine segment.
Conclusion Color Doppler sonography is highly
sensitive and
specific in the antenatal diagnosis of placenta previa
accreta .If a strong suspicion is found before delivery
,appropriate location and timing for delivery should be
considered ,to allow access to adequate surgical
personel and equipment ,preoperative blood
preparation to reduce morbidity and mortality.
Journal title
Ultrasound in obstetric and gynecology ISSUN 0960-
7692 Source 2000 Vol 15 In 1 PP. 28-35 (22rel)
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During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
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