Introduction. Retroperitoneal ectopic pregnancy is extremely rare, but potentially fatal condition due to possible massive hemorrhage, representing a great challenge to clinicians. Case report. We presented early retroperitoneal pregnancy in a patient with previous caesarean section, diagnosed at the sixth gestational week, located in the left broad ligament, primary treated by laparoscopy, which had to be converted to laparotomy due to massive intraoperative bleeding from the implantation site. Conclusion. High index of suspicion, combined with carefully interpreted clinical and ultrasound findings are crucial for the timely diagnosis of retroperitoneal pregnancy, before the occurrence of severe bleeding. The rising, even plateau of serum ?-human chorionic gonadotropin (?-HCG) levels without identification of uterine or ectopic (tubal) pregnancy should cause suspicion on ectopic pregnancy in unusual location.
If there are contrain-ications to menopausal hormone therapy or patients are unwilling to take hormone therapy, alternative treatments, which canlalso solve menopausal symptoms, should be considered.
The use and development of Artificial Intelligence (AI) based systems is becoming increasingly prominent in different industries. The aviation industry is also gradually adopting AI-based systems, for instance, with Machine Learning algorithms for flight assistance. There are several reasons why adopting these technologies poses additional obstacles in aviation compared to other industries. One reason are the strong safety requirements which lead to obligatory and thorough assurance activities such as testing to obtain certification. Therefore, a systematic approach is needed for developing, deploying, and assessing test cases for AI-based systems in aviation. This paper proposes a method for iterative scenario-based testing for AI-based systems. The method contains three major parts: First, a high-level description of test scenarios; second, the generation and execution of these scenarios; and last, monitoring of parameters during scenario execution. Parameters are refined, and the steps are repeated iteratively. The method forms a basis for developing iterative scenariobased testing solutions. As a domain-specific example, a practical implementation of this method is illustrated. For an object detection application used on an airplane, flight scenarios, including multiple airplanes are generated from a descriptive scenario model and executed in a simulation environment. The parameters are monitored using a custom Operational Design Domain monitoring tool and refined in the process of iterative scenario generation and execution. The proposed iterative scenariobased testing method helps in generating precise test cases for AI-based systems while having a high potential for automation.
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