Impact of letrozole versus clomiphene citrate on endometrial receptivity in Iraqi women with polycystic ovarian syndrome.

WHAT IS KNOWN AND OBJECTIVES: Letrozole is widely known for its use as an ovulation inductor. This study aims to investigate the effects of letrozole and clomiphene citrate in females with polycystic ovarian syndrome. METHODS: This is a randomized non-blinded controlled trial study that included 80 infertile females with polycystic ovarian syndrome receiving a standard dose of either clomiphene citrate or letrozole on day 2 of the cycle. An ultrasound was done to examine growth of the follicle, endometrial thickness on days 12-13, and a Doppler study to measure resistance index (RI), pulsatility index and ratio of systolic/diastolic velocity. RESULTS AND DISCUSSION: The mean levels of dominant follicle and oestradiol were significantly higher in the clomiphene citrate group than in the letrozole group. The letrozole group had a significantly greater endometrial thickness than the clomiphene citrate group. The resistance index and pulsatility index were lower in the letrozole group and in pregnant women than in the clomiphene citrate group and the non-pregnant group. WHAT IS NEW AND CONCLUSION: The use of letrozole for ovulation induction in polycystic ovarian syndrome patients has a better effect on endometrial receptivity markers when compared to clomiphene citrate.

An efficient edge/cloud medical system for rapid detection of level of consciousness in emergency medicine based on explainable machine learning models.

Emergency medicine (EM) is one of the attractive research fields in which researchers investigate their efforts to diagnose and treat unforeseen illnesses or injuries. There are many tests and observations are involved in EM. Detection of the level of consciousness is one of these observations, which can be detected using several methods. Among these methods, the automatic estimation of the Glasgow coma scale (GCS) is studied in this paper. The GCS is a medical score used to describe a patient's level of consciousness. This type of scoring system requires medical examination that may not be available with the shortage of the medical expert. Therefore, the automatic medical calculation for a patient's level of consciousness is highly needed. Artificial intelligence has been deployed in several applications and appears to have a high performance regarding providing automatic solutions. The main objective of this work is to introduce the edge/cloud system to improve the efficiency of the consciousness measurement through efficient local data processing. Moreover, an efficient machine learning (ML) model to predict the level of consciousness of a certain patient based on the patient's demographic, vital signs, and laboratory tests is proposed, as well as maintaining the explainability issue using Shapley additive explanations (SHAP) that provides natural language explanation in a form that helps the medical expert to understand the final prediction. The developed ML model is validated using vital signs and laboratory tests extracted from the MIMIC III dataset, and it achieves superior performance (mean absolute error (MAE) = 0.269, mean square error (MSE) = 0.625, R 2 score = 0.964). The resulting model is accurate, medically intuitive, and trustworthy.