Marchi Guido, Mercier Mattia, Cefalo Jacopo, Salerni Carmine, Ferioli Martina, Candoli Piero, Gori Leonardo, Cucchiara Federico, Cenerini Giovanni, Guglielmi Giacomo, Mondoni Michele
Pulmonology Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, Pisa, Italy (
Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital IRCCS, Full Member of European Reference Network on Rare and Complex Epilepsies EpiCARE, Rome, Italy.
Eur Respir Rev. 2025 Apr 2;34(176). doi: 10.1183/16000617.0263-2024. Print 2025 Apr.
Pleural diseases represent a significant healthcare burden, affecting over 350 000 patients annually in the US alone and requiring accurate diagnostic approaches for optimal management. Traditional imaging techniques have limitations in differentiating various pleural disorders and invasive procedures are usually required for definitive diagnosis.
We conducted a nonsystematic, narrative literature review aimed at describing the latest advances in imaging techniques and artificial intelligence (AI) applications in pleural diseases.
Novel ultrasound-based techniques, such as elastography and contrast-enhanced ultrasound, are described for their promising diagnostic accuracy in differentiating malignant from benign pleural lesions. Quantitative imaging techniques utilising pixel-density measurements to noninvasively distinguish exudative from transudative effusions are highlighted. AI algorithms, which have shown remarkable performance in pleural abnormality detection, malignant effusion characterisation and automated pleural fluid volume quantification, are also described. Finally, the role of deep-learning models in early complication detection and automated analysis of follow-up imaging studies is examined.
Advanced imaging techniques and AI applications show promise in the management and follow-up of pleural diseases, improving diagnostic accuracy and reducing the need for invasive procedures. However, larger prospective studies are needed for validation. The integration of AI-driven imaging analysis with molecular and genomic data offers potential for personalised therapeutic strategies, although challenges in data privacy, algorithm transparency and clinical validation persist. This comprehensive approach may revolutionise pleural disease management, enhancing patient outcomes through more accurate, noninvasive diagnostic strategies.
胸膜疾病带来了巨大的医疗负担，仅在美国每年就影响超过35万名患者，需要准确的诊断方法以实现最佳管理。传统成像技术在区分各种胸膜疾病方面存在局限性，通常需要侵入性检查才能做出明确诊断。
我们进行了一项非系统性的叙述性文献综述，旨在描述胸膜疾病成像技术和人工智能（AI）应用的最新进展。
介绍了基于超声的新技术，如弹性成像和超声造影，它们在鉴别恶性和良性胸膜病变方面具有有前景的诊断准确性。强调了利用像素密度测量来无创区分渗出性和漏出性胸腔积液的定量成像技术。还描述了在胸膜异常检测、恶性胸腔积液特征分析和胸腔积液自动定量方面表现出色的AI算法。最后，研究了深度学习模型在早期并发症检测和后续成像研究自动分析中的作用。
先进的成像技术和AI应用在胸膜疾病的管理和随访中显示出前景，提高了诊断准确性并减少了侵入性检查的需求。然而，需要更大规模的前瞻性研究来进行验证。尽管在数据隐私、算法透明度和临床验证方面仍然存在挑战，但将AI驱动的成像分析与分子和基因组数据相结合为个性化治疗策略提供了潜力。这种综合方法可能会彻底改变胸膜疾病的管理，通过更准确、无创的诊断策略改善患者预后。
