Publications by authors named "Tarig Elhakim"
Recent trends in representation of female radiologists in radiology annual scientific meetings.
Curr Probl Diagn Radiol
November 2024
Article Synopsis
- The study aimed to assess how well women were represented as speakers at U.S. radiology annual scientific meetings from 2019 to 2022.
- At the Society of Interventional Radiology (SIR), female speaker representation rose from 20.0% to 26.5%, while the American Roentgen Ray Society (ARRS) saw fluctuations, peaking at 48.5% in 2022 after dropping in previous years.
- The findings highlight a general improvement in female speaker representation, but emphasize the need for ongoing advocacy and strategies to maintain and promote gender equality in radiology.
Article Synopsis
- The study aimed to find a connection between machine learning-analyzed CT body composition and 90-day mortality rates after a medical procedure called transjugular intrahepatic portosystemic shunt (TIPS), while also comparing it to the existing MELD score for mortality prediction.
- Researchers conducted a retrospective analysis involving 122 patients who had CT scans before their TIPS procedure and were tracked for at least 90 days post-surgery.
- Results showed that higher MELD scores and lower skeletal muscle and fat measurements were associated with increased 90-day mortality, indicating that certain body composition metrics could help predict patient outcomes alongside the MELD score.
Article Synopsis
- The study evaluated how well GPT-4 could create understandable patient education materials for common interventional radiology procedures.
- A total of 10 procedures were assessed, with input from both clinical physicians and nonclinical assessors to measure clarity, appropriateness, and readability.
- Results indicated GPT-4 generated instructions that were generally appropriate and easier to read than existing patient resources, showing better accessibility for patients in terms of language complexity.
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning.
View Article and Find Full Text PDFArtificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising.
View Article and Find Full Text PDFSpontaneous pneumomediastinum (SPM) and pneumothorax (PNX) unrelated to positive pressure ventilation has been recently reported as an unusual complication in cases of severe COVID-19 pneumonia. The presumed pathophysiological mechanism is diffuse alveolar injury leading to alveolar rupture and air leak. We present a case of COVID-19 pneumonia complicated on day 13 post admission by SPM, PNX and subcutaneous emphysema in a patient with no identifiable risk factors for such complication.
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