BACKGROUND: The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has urgently necessitated effective therapeutic solutions, with a focus on rapidly identifying and classifying poten...
Journal of imaging informatics in medicine
Mar 8, 2024
A significant challenge in machine learning-based medical image analysis is the scarcity of medical images. Obtaining a large number of labeled medical images is difficult because annotating medical images is a time-consuming process that requires sp...
It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA...
Artificial Intelligence (AI) applications have the potential to revolutionize conventional healthcare practices, creating a more efficient and patient-centred approach with improved outcomes. This guide discuses eighteen AI-based applications in clin...
Conventional COVID-19 testing methods have some flaws: they are expensive and time-consuming. Chest X-ray (CXR) diagnostic approaches can alleviate these flaws to some extent. However, there is no accurate and practical automatic diagnostic framework...
While it is common to monitor deployed clinical artificial intelligence (AI) models for performance degradation, it is less common for the input data to be monitored for data drift - systemic changes to input distributions. However, when real-time ev...
Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limiting such as the common cold and catarrh, ...
BACKGROUND: Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model...
Journal of medical imaging and radiation sciences
Feb 24, 2024
Uncertainty regarding the future of radiologists is largely driven by the emergence of artificial intelligence (AI). If AI succeeds, will radiologists continue to monopolize imaging services? As AI accuracy progresses with alacrity, radiology reads w...
BACKGROUND AND OBJECTIVES: We aimed to analyze the study designs, modeling approaches, and performance evaluation metrics in studies using machine learning techniques to develop clinical prediction models for children and adolescents with COVID-19.