Ureteral Stent Retrieval Using the Crochet Hook Technique in Females

INTRODUCTION: We developed a method for ureteral stent removal in female patients that requires no cystoscopy or fluoroscopic guidance using a crochet hook. In addition, we also investigated the success rate, complications and pain associated with this procedure. METHODS: A total of 40 female patients (56 stents) underwent the removal of ureteral stents. All procedures were carried out with the patients either under anesthesia, conscious sedation, or analgesic suppositories as deemed appropriate for each procedure including Shock Wave Lithotripsy (SWL), Ureteroscopy (URS), Percutaneous Nephrolithotomy (PCNL), and ureteral stent removal. At the time of these procedures, fluoroscopy and/or cystoscopy were prepared, but they were not used unless we failed to successfully remove the ureteral stent using the crochet hook. In addition, matched controls (comprising 50 stents) which were removed by standard ureteral stent removal using cystoscopy were used for comparison purposes. RESULTS: A total of 47 of the 56 stents (83.9%) were successfully removed. In addition, 47 of 52 (90.4%) were successfully removed except for two migrated stents and two heavily encrusted stents which could not be removed using cystoscopy. Ureteral stent removal using the crochet hook technique was unsuccessful in nine patients, including two encrustations and two migrations. Concerning pain, ureteral stent removal using the crochet hook technique showed a lower visual analogue pain scale (VAPS) score than for the standard technique using cystoscopy. CONCLUSIONS: Ureteral stent removal using a crochet hook is considered to be easy, safe, and cost effective. This technique is also easy to learn and is therefore considered to be suitable for use on an outpatient basis

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance