Assisted Reproductive Technology (ART) includes in vitro fertilization-embryo transfer (IVF-ET), gamete intrafallopian transfer (GIFT), zygote intrafallopian transfer (ZIFT), and frozen embryo transfer (FET). These techniques also apply to oocyte donation and gestational carriers. Approximately 99 percent of ART cycles performed are IVF-ET. IVF-ET has helped many couples conceive successfully. ART may be recommended when other treatments (such as intrauterine insemination) have not been successful or when there is severe male factor infertility, severe endometriosis or tubal obstruction. Fertility therapy generates a wide range of emotions. Waiting, uncertainty, and the demands of treatment can lead to feelings of frustration, confusion, and resentment. We believe that the more you know about the tests and treatments, the less anxiety and concern you will feel about your condition. If you are going through this process with a partner, please review this information together. Most centers utilize a multi-disciplinary professional team. During your treatment cycle, many team members, in addition to your individual physician, will participate in your care. Advanced infertility procedures require time and energy, as well as an emotional and financial commitment. The entire staff should make every effort to provide you with personal and compassionate care in order to make this difficult time as comfortable and manageable as possible.
A retrospective study was conducted in women with history of a caesarean scar pregnancy (CSP) to explore the possible causative mechanisms. Over a period of 40 months, a total of 16,926 deliveries and 3554 caesarean sections (CS) occurred in our hospital. Nine cases of CSP were identified at an incidence of 1:1880 births and 0.25% of all CS. Analysis of the indications of the previous CS revealed that 88.8% of women with known indication had undergone CS without labour. Out of them, 75% underwent CS at preterm gestation and 25% had term elective procedure for breech presentation. Of the patients, 77.7% had multiple CS. Conservation of the uterus was possible in 77.7% of patients utilising non-radical forms of treatment. As most of the women underwent CS with a non-contractile uterus without labour, we believe that the risk of CSP may be related to the indications of the previous CS as the number of CS alone could not explain the occurrence of CSP. It is time to explore this area so that screening strategies can be developed to detect CSP at the earliest possible gestation and to prevent life-threatening complications such as uterine rupture and severe hemorrhage.
Statement of the Problem: The World Health Organization announced in 2018 that 422 million people worldwide suffer from diabetes mellitus. The projected impact of vision impairment and blindness caused by Diabetic Retinopathy (DR) will end in significant public health and economic consequences. DR is preventable and treatable if detected early through an annual eye screening. However, screening rates are low globally thanks to a paucity of trained eye-health professionals in developing countries and in rural or remote areas of developed countries. Method: Based on the research from CSIRO Australian e-Health Research Centre, TeleMedC group commercialized an AIbased Diabetic Retinopathy screening system-DR grader, an automatic DR grading and preliminary referral decision support tool for patients with diabetes. The cloud-based tele-ophthalmology framework has the functionalities of: (1) Deep learning based picture quality evaluation device; (2) Deep learning based DR ???sickness/no malady??? evaluating for shading retinal pictures; (3) DR injury limitation and DR level sign; (4) Preliminary report of patient referral/no referral choice; and (5) DR infection review by eye specialists and creating understanding referral pathway. DR grader has been deployed in a GP Super clinic at Midland, Western Australia from December 2016 onwards. Results: Results of this implementation were published during a JAMA Network Open article (September 2018) evaluated a complete of 291 patients. The system correctly identified all 12 patients with true disease (sensitivity 100%) and misclassified 23 patients as having disease (specificity 92%). The DR grader has been undergoing testing in Singapore since early 2018 at the Department of Ophthalmology, National University Hospital and in 30 GP clinics with similar or better preliminary results pending publication. Conclusion: The AI-based DR screening system provides quick DR patient referral decision support within the medical care setting. It benefits patients from poorly-resourced and underserved remote areas for its low cost, time savings and high patient acceptability. The system was well received by primary care providers.
