With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women’s physical and mental health. Early breast cancer screening—through mammography, ultrasound, or magnetic resonance imaging (MRI)—can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

Purpose Coronavirus 2019 (COVID-19) had major social, medical, and economic impacts globally. The study aims to develop a deep-learning model that can predict the severity of COVID-19 in patients based on CT images of their lungs. Methods COVID-19 causes lung infections, and qRT-PCR is an essential tool used to detect virus infection. However, qRT-PCR is inadequate for detecting the severity of the disease and the extent to which it affects the lung. In this paper, we aim to determine the severity level of COVID-19 by studying lung CT scans of people diagnosed with the virus. Results We used images from King Abdullah University Hospital in Jordan; we collected our dataset from 875 cases with 2205 CT images. A radiologist classified the images into four levels of severity: normal, mild, moderate, and severe. We used various deep-learning algorithms to predict the severity of lung diseases. The results show that the best deep-learning algorithm used is Resnet101, with an accuracy score of 99.5% and a data loss rate of 0.03%. Conclusion The proposed model assisted in diagnosing and treating COVID-19 patients and helped improve patient outcomes.

Magnetic Resonance Imaging (MRI) is increasingly becoming a cornerstone in modern diagnostic healthcare, offering unparalleled capabilities in stroke, dementia, and cancer screening. Therefore, this study aims to map medical MRI literature affiliated with Arab countries, focusing on publication trends, top journals, author affiliations, study countries, and authors' collaboration, and keyword analysis. The scientific database used is the Scopus database. Microsoft Excel, VOSviewer software, and Biblioshiny for the Bibliometrix R package are the bibliometric tools used in this analysis. A total of 2592 publications were published between 1988 and 2022, with total citations of 22,115. Most of them were original articles (91,7%) and 89.9% were published in traditional journals. The number of total publications exhibited a steady increase over time, whereas total citations showed fluctuations, peaking in 2015 with 1571 citations for publications from that year. The most cited article was authored by Yaseen M. Arabi, receiving 286 citations. Saudi Arabia was the top active country. In addition, the most prolific author was Maha S Zaki, and the most prolific source was the “Egyptian Journal of Radiology and Nuclear Medicine”. The most prolific affiliation was Cairo University. The “multiple sclerosis” and “case report” were the most trending keywords. The analysis revealed a significant growth in MRI research inside Arab countries, as shown by an increase in the total number of publications and international collaborations. Despite these developments, the results of this study suggest that there is still room for MRI research in the Arab region to advance. This can be achieved through increasing international collaboration and multidisciplinary work.

Bilateral spontaneous quadriceps tendon rupture is a rare condition characterized by the simultaneous tear of the fibrous tissue connecting the quadriceps muscle to the patella bone. Prompt diagnosis is crucial for appropriate treatment and optimal outcomes. We present a case of a 70-year-old male with bilateral knee pain and an inability to walk, resulting from a trivial fall. Despite initial misdiagnosis, a thorough evaluation, including physical examination and imaging, revealed bilateral quadriceps tendon rupture. Surgical repair was performed, followed by a comprehensive rehabilitation program. At the four-month follow-up, the patient showed significant improvement in pain and function. This article provides a comprehensive review of the existing literature on bilateral quadriceps tendon rupture, emphasizing the challenges in the diagnosis and management of this rare condition. Early diagnosis, prompt surgical intervention, and a tailored rehabilitation program are crucial for successful outcomes.

Aim To compare the accuracy of shoulder ultrasound (US) in diagnosing rotator cuff tendon tears between junior and experienced musculoskeletal (MSK) radiologists. Also, to compare the overall ultrasound accuracy referenced to MRI. Methods A retrospective data collection for patients with clinically suspected rotator cuff tears who underwent ultrasound from June 2021 - June 2023 was conducted. Patients who also performed MRI for the same shoulder were only included in the study. US and MRI images were evaluated by two MSK radiologists with different experience levels. The diagnosis of rotator cuff tears was done on MRI through consensus. Ultrasound accuracy referenced to MRI was calculated for each radiologist. A second consensus was conducted for US images to calculate the overall US accuracy. The percentage of agreement and Cohen's kappa correlation coefficient were calculated before and after the US consensus. Results Forty-one patients were included in the study, 12 (29.3%) males and 29 (70.7%) females, with a mean age of 49.6 years. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and accuracy of US interpreted by junior vs experienced MSK radiologists for supraspinatus full thickness tears (FTTs) were 100% vs 91%, 90% vs 93%, 79% vs 83%, 100% vs 97%, and 93% vs 93%, respectively. After the second consensus, sensitivity, specificity, PPV and NPV, and accuracy were 91%, 90%, 77%, 96%, and 90%, respectively. Conclusion The accuracy of shoulder ultrasound in diagnosing supraspinatus FTTs by junior MSK radiologists compares well to the more experienced ones, but not for partial thickness tears (PTTs).