Geran Maule, Ahmad Alomari, Abdallah Rayyan, Ogbeide Aghahowa, Mohammad Khraisat, Luis Javier
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Harnessing AI for Improved Detection and Classification of Pleural Effusion: Insights and Innovations.
The detection and classification of pleural effusion present significant challenges in clinical practice, often contributing to delayed diagnoses and suboptimal patient outcomes. Recent advancements in artificial intelligence (AI) and machine learning (ML) techniques hold substantial promise for enhancing the accuracy and efficiency of pleural effusion diagnostics. This paper reviews the current landscape of AI applications in pleural effusion detection, synthesizing findings across diverse studies to illustrate the transformative potential of these technologies. We examine various ML models, including deep learning and ensemble methods, that leverage clinical, laboratory, and imaging data to improve diagnostic performance. Notably, models such as Light Gradient Boosting Machine (LGB) and XGBoost have achieved accuracy levels up to 96% and high AUC values (e.g., AUC = 0.883 for pleural effusion differentiation). This overview highlights the importance of integrating diverse diagnostic parameters to enhance pleural effusion diagnostic accuracy and outlines future research directions essential for optimizing patient management and outcomes.
期刊介绍:
Canadian Respiratory Journal is a peer-reviewed, Open Access journal that aims to provide a multidisciplinary forum for research in all areas of respiratory medicine. The journal publishes original research articles, review articles, and clinical studies related to asthma, allergy, COPD, non-invasive ventilation, therapeutic intervention, lung cancer, airway and lung infections, as well as any other respiratory diseases.