Enhancing Clinical Diagnosis With Convolutional Neural Networks: Developing High-Accuracy Deep Learning Models for Differentiating Thoracic Pathologies.

Background The use of computational technology in medicine has allowed for an increase in the accuracy of clinical diagnosis, reducing errors through additional layers of oversight. Artificial intelligence technologies present the potential to further augment and expedite the accuracy, quality, and efficiency at which diagnosis can be made when used as an adjunctive tool. Such techniques, if found to be accurate and reliable in their diagnostic acuity, can be implemented to foster better clinical decision-making, improving patient quality of care while reducing healthcare costs.

Deep Learning Classification of Tuberculosis Chest X-rays.

Background Tuberculosis (TB) is an infectious disease caused by the bacterium . It primarily affects the lungs but can also affect other organs, such as the kidneys, bones, and brain. TB is transmitted through the air when an infected individual coughs, sneezes, or speaks, releasing tiny droplets containing the bacteria.

Pneumoperitoneum in a Patient With COVID-19 and Acute Respiratory Distress Syndrome Without Pneumothorax.

We present a case report of pneumoperitoneum, pneumomediastinum, and subcutaneous emphysema in a patient with COVID-19 pneumonia-causing acute respiratory distress syndrome (ARDS) without any pneumothorax occurring. Pneumothorax, pneumomediastinum, and subcutaneous emphysema are known complications of barotrauma due to positive pressure from mechanical ventilation which is necessary for patients suffering from a severe case of COVID-19. In our literature search, we could not find any reported case of pneumoperitoneum without pneumothorax occurring.