Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions. INDEX TERMS Chest x-ray, coronavirus, COVID-19, deep learning, radiological imaging.

Objectives: To evaluate the general knowledge among primary health care (PHC) physicians regarding the management of common urological problems in Saudi Arabia. Methods: This is an observational prospective study, where a self-administered questionnaire was distributed to practicing PHC physicians in the western region of Saudi Arabia on January 2017. The questionnaire consisted of 21-item questions, inquiring about demographics and general urological knowledge and skills. The management of common urological problems was assessed by case scenarios for specific urological condition, including urethral catheterization, definition and evaluation of hematuria, recognition of age-specific increase in prostatic specific antigen (PSA), and management of lower urinary tract symptoms. Results: A total of 148 questionnaires were distributed, with a response rate of 75.7%, where 112 respondents completed the questionnaires, including 54.3% residents, 39% general practitioners, and 5.4% specialists. Fifty-seven percent of respondents were males and 68% were Saudi practitioners. A higher number of respondents expressed that they were able to catheterize a male than female patient (56.5% versus 34.3%). Only 6.4% of respondents defined microscopic hematuria accurately. Knowledge about hematuria, serum prostate specific antigen and overactive bladder was low in all groups. Apart from hematuria, seeking urological consultations was less than 35% for all other disease entities. Conclusion: Urological knowledge among PHC physicians seems to be insufficient. Significant percentages of the participants were unable to catheterize a female patient, did not know the definition of hematuria; and whether to ask for urological consultations in cases of hematuria, increased PSA, and overactive bladder.

Background:Nephrolithiasis is a common condition that has various classifications according to stone composition. Stone formation can affect renal function; it can be a strong risk factor for chronic kidney disease (CKD). The main objective of this study is to explore the association between creatinine clearance and different stone compositions.Methods:This is a retrospective cohort study conducted in a tertiary center in Jeddah, Saudi Arabia, between 2005 and 2014. Renal function was assessed by the estimating glomerular filtration rate (eGFR) by the Cockcroft-Gault equation. Stone composition was determined by urinary calculi analysis with infrared spectrometry.Results:Stones of 365 patients, with a mean age of 48.2 ± 13.6 years and a male to female ratio of 3.2:1, were analyzed. Stage 2 CKD has been documented. It involved oxalate, struvite, cystine, and uric acid stones. The worst eGFR was reported for stones containing uric acid. The eGFR was least affected with apatite stones followed by brushite stones.Conclusion:Stone disease can affect renal function. Different stone compositions show factor for renal impairment, and this should be considered in patient management. A special precaution should be considered for higher risk groups. Multidisciplinary patient care and immediate referral to a nephrologist are strongly advised.

UNSTRUCTURED Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions