Hysteroscopy is a widely used endoscopic method, the "gold standard" for the examination of uterine cavity changes. Office hysteroscopy is an intervention that makes the accomplishment of ambulant examination, opposite to the traditional method. Due to the small diameter of the device the anesthesia is unnecessary, because there is no need of the dilatation of the cervix. Indications of the examination are wide-ranging. Besides the abnormal uterine bleeding, it can be used in the examination of infertility, and those intrauterine changes (polyp, submucosus myoma, adhesion), that were diagnosed by other imagining methods that cause infertility complaints. The aim of our present review is recommend the use of this method because it does not need any preparation and it is minimal invasive. The use of traditional method that needs longer preparation, observation, anesthesia and operating theatre, is only suggested in cases of proven pathology.
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http://dx.doi.org/10.1556/OH.2011.28997 | DOI Listing |
Afr J Reprod Health
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Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing , China.
Giant ovarian cysts (GOCs) have become less common in developed countries due to routine health screenings, but they remain prevalent in economically underdeveloped regions. Treatment options for GOCs depend on factors such as age, cyst characteristics, and pregnancy status. Minimally invasive single-port laparoscopic surgery has largely replaced traditional open surgery due to its aesthetic advantages and effectiveness.
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Department of Sciences and Technologies for Sustainable Development and One Health, Universita Campus Bio-Medico di Roma, 00128 Rome, Italy.
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Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
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Human Papillomavirus (HPV) infection is a significant global health concern linked to various cancers, particularly cervical cancer. Timely and accurate detection of HPV is crucial for effective management and prevention strategies. Traditional laboratory-based HPV testing methods often suffer from limitations such as long turnaround times, restricted accessibility, and the need for trained personnel, especially in resource-limited settings.
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In response to the COVID-19 pandemic, Israel prioritized pregnant women for vaccination, recognizing them as a high-risk group. This study aims to explore factors influencing the acceptance of Influenza, Pertussis (T-dap), and COVID-19 vaccines among pregnant women, focusing on attitudes, social norms, perceived control, and risk perceptions. Additionally, the study compares acceptance patterns between traditional vaccines and the newer COVID-19 vaccine.
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