The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

Background Hepatocellular carcinoma (HCC) is the most common primary liver malignancy that is strongly associated with chronic liver disease. Isolated hepatic tuberculosis is an uncommon type of tuberculosis. Concomitant occurrence of both conditions is extremely rare. Case presentation We report the case of a 47-year-old man who presented with fever and abdominal pain for 3 months prior to presentation. He reported a history of anorexia and significant weight loss. Abdominal examination revealed a tender, enlarged liver. Abdominal computed tomography (CT) demonstrated a solid heterogeneous hepatic mass with peripheral arterial enhancement, but no venous washout, conferring a radiological impression of suspected cholangiocarcinoma. However, a CT-guided biopsy of the lesion resulted in the diagnosis of concomitant HCC and isolated hepatic tuberculosis. Conclusion A rapid increase in tumor size should draw attention to the possibility of a concomitant infectious process. Clinicians must have a high index of suspicion for tuberculosis, especially in patients from endemic areas, in order to initiate early and proper treatment.

Background Pulmonary embolism (PE) is a common life-threatening medical emergency that needs prompt diagnosis and management. Providing urgent care is a key determinant of quality in the emergency department (ED) and time-based targets have been implemented to reduce length of stay and overcrowding. The study aimed to determine factors that are associated with having a time-to-disposition of less than 4 h in patients with suspected PE who underwent computed tomography pulmonary angiography (CT-PA) to confirm the diagnosis. Methods After obtaining approval from the ethics committee, we conducted a retrospective observational study by examining CT-PA scans that was performed to rule out PE in all adult patients presenting at the ED between January 2018 and December 2019. Demographic information and clinical information, as well as arrival and disposition times were collected from electronic health records. Multivariable regression analysis was used to identify the independent factors associated with meeting the 4-h target in the ED. Results In total, the study involved 232 patients (76 men and 156 women). The median length of stay in the ED was 5.2 h and the 4-h target was achieved in 37% of patients. Multivariable logistic regression analysis revealed that a positive CT-PA scan for PE was independently associated with meeting the four-hour target in the ED (odds ratio [OR]: 2.2; 95% CI: 1.1–4.8). Furthermore, Hemoptysis was the only clinical symptom that served as an independent factor associated with meeting the 4-h target in the ED (OR: 10.4; 95% CI: 1.2–90.8). Conclusion Despite the lower number of staff and higher volume of patients on weekends, patients who presented on weekends had shorter stays and were more likely to meet the 4-h target. Careful clinical assessment, prior to requesting a CT-PA scan, is crucial, since negative CT-PA scans may be associated with failure to meet the 4-h target.