One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical/clinical signs in addition to the patient’s symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features.

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.

Pulmonary embolism (PE) is a common life-threatening condition with non-specific clinical presentations. The diagnosis of PE depends highly on imaging studies, which may also provide prognostic information. This study aimed to describe the clinical and imaging profiles of patients with PE, emphasizing the differences between central and peripheral PE. After ethics review board approval, this retrospective observational study examined the non-negative results in adult patients who underwent computed tomography pulmonary angiography (CT-PA) at our hospital between May 2016 and December 2019. Demographic and clinical information and imaging findings were collected from the electronic medical records. The study included 85 cases that were identified after re-interpreting the 103 non-negative CT-PA scans. Six cases were excluded for incomplete data and 12 cases were false-positive. Central PE was found in 63.5% of the cases. Obesity was the most common risk factor seen in 37.6% of the cases. Furthermore, 9.4% of the patients had sickle cell disease, which tended to be associated with peripheral PE. There was no difference between the peripheral and central PE in most clinical and imaging parameters evaluated (P > 0.05). However, patients with isolated subsegmental PE were more likely to develop hemoptysis (P = 0.04). This study suggests that patients with obesity and sickle cell disease constitute an important proportion of all PE cases. Furthermore, the clinical and imaging profiles in patients with peripheral PE are similar to those in patients with central PE. Future research should focus on the clinical value of peripheral PE in patients with sickle cell disease.

Background: Family medicine physicians may encounter a wide variety of conditions, including acute and urgent cases. Considering the limited access to diagnostic investigations in primary care practice, chest X-ray remains the imaging modality of choice. The current study assessed the competency of family medicine residents in the interpretation of chest X-rays for emergency conditions and to compare it with that of diagnostic radiology residents, general practitioners, and medical interns. Methods: An online survey was distributed to 600 physicians, including family medicine residents, medical interns, general practitioners, and diagnostic radiology residents. The study included some background information such as gender, years in practice, training type, interest in pulmonary medicine and diagnostic radiology, and having adequate training on the interpretation of chest X-rays. The survey had 10 chest X-ray cases with brief clinical information. Participants were asked to choose the most likely diagnosis and to rate their degree of confidence in the interpretation of the chest X-ray for each case. Results: The survey was completed by 205 physicians (response rate = 34.2%). The overall diagnostic accuracy was 63.1% with a significant difference between family medicine and radiology residents (58.0% vs. 90.5%; P < 0.001). The COVID-19 pneumonia (85.4%) and pneumoperitoneum (80.5%) cases had the highest diagnostic accuracy scores. There was a significant correlation between the diagnostic confidence and accuracy (r_s = 0.39; P < 0.001). Multivariable regression analysis revealed that being diagnostic radiology residents (odds ratio [OR]: 13.0; 95% confidence interval [CI]: 2.5–67.7) and having higher diagnostic confidence (OR: 2.2; 95% CI: 1.3–3.8) were the only independent predictors of achieving high diagnostic accuracy. Conclusion: The competency of family medicine residents in the interpretation of chest X-ray for emergency conditions was far from optimal. The introduction of radiology training courses on emergency conditions seems imperative. Alternatively, the use of tele-radiology in primary healthcare centers should be considered.

Background. Adrenal cavernous hemangiomas (AH) are benign nonfunctional vascular tumors rarely discovered as incidental findings on imaging studies or autopsies. This study presents a single case report of AH with another rare finding of the Liesegang ring. Also, we reviewed 73 case reports of cavernous adrenal hemangioma to provide an overview of AHs clinical characteristics. Case Report. A nonfunctional AH was incidentally discovered in a 59-year-old morbidly obese female patient with a 10-year history of hypertension and thyroidectomy. An abdominal computed tomography (CT) scan showed a left suprarenal mass of 16cm in diameter. While the patient had no clinical manifestations from the hemangioma, all laboratory tests were within the normal values with no indication of a functional adrenal tumor. The mass was removed by open left adrenalectomy. The microscopic histological examination revealed a laminated structure with wide blood-filled spaces with a central core of necrotic and hemorrhagic changes, characteristic of a cavernous AH with the presence of a rare Liesegang ring. Conclusion. Although rare, AH should be considered as a differential diagnosis for adrenal masses. This is the first reported case of a cavernous AH with rare microscopic findings of the Liesegang ring.