利用人工智能改进胸腔积液的检测和分类:见解和创新。

IF 2.1 4区 医学 Q3 RESPIRATORY SYSTEM
Canadian respiratory journal Pub Date : 2025-08-06 eCollection Date: 2025-01-01 DOI:10.1155/carj/2882255
Geran Maule, Ahmad Alomari, Abdallah Rayyan, Ogbeide Aghahowa, Mohammad Khraisat, Luis Javier
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引用次数: 0

摘要

胸腔积液的检测和分类在临床实践中面临着重大挑战,经常导致诊断延迟和患者预后不佳。人工智能(AI)和机器学习(ML)技术的最新进展为提高胸腔积液诊断的准确性和效率带来了巨大的希望。本文回顾了人工智能在胸腔积液检测中的应用现状,综合了不同研究的发现,以说明这些技术的变革潜力。我们研究了各种ML模型,包括深度学习和集成方法,这些模型利用临床、实验室和成像数据来提高诊断性能。值得注意的是,光梯度增强机(LGB)和XGBoost等模型的准确率高达96%,AUC值很高(例如,胸腔积液鉴别的AUC = 0.883)。这篇综述强调了整合多种诊断参数以提高胸腔积液诊断准确性的重要性,并概述了优化患者管理和结果的未来研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Canadian respiratory journal
Canadian respiratory journal 医学-呼吸系统
CiteScore
4.20
自引率
0.00%
发文量
61
审稿时长
6-12 weeks
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.
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