We present a case of a one month old female infant who presented with left inguinal swelling. She was prematurely born at 32 weeks of gestational age. Preoperative ultrasound showed features of sliding indirect inguinal hernia with both ovaries, fallopian tubes and uterus; which were also evident per operatively. Patient underwent successful hernia repair and uneventful post-operative period. Patient is on follow up currently. In conclusion, we attempt to highlight the exiguous prevalence of inguinal hernia with uterus, fallopian tubes and ovaries; which has been sparsely reported in the literature and also the importance of preoperative of sonography.

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March–June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.