肺部超声(lung ultrasound，LUS)检查因无辐射、低成本、实时成像、床旁可操作且无转运风险，在急诊重症监护病房患者的诊疗中具有重要的应用价值。LUS使用B型和M型模式评估肺部病变，使用LUS评分量化“B线”和评估病变程度，提供了一个系统且客观的病变评价体系。自2008年以来，法国重症医学家Lichtenstein等提出的急诊床旁LUS检查(bedside lung ultrasonography in emergency, BLUE)方案及其后续改良的BLUE（bedside lung ultrasonography in emergency-plus, BLUE-plus）方案在评估急性呼吸衰竭中取得了显著成果。BLUE方案通过不同的超声表现类型区分呼吸困难病因，诊断心源性肺水肿的灵敏度为97%，特异度为95%；诊断肺炎的灵敏度为89%，特异度为94%。BLUE-plus方案增加了后背区域扫描，将诊断肺不张和肺实变的灵敏度提升至95.71%，特异度为87.50%。凭借LUS检查中的“胸膜滑动征”和“B线”等指标，诊断肺水肿的灵敏度可达96%，远超胸部X线(chest X-ray, CXR)检查65%的灵敏度，还可区分心源性与非心源性肺水肿。LUS检查诊断胸腔积液的灵敏度达92%，并能估算积液量。对于肺实变，LUS检查表现出80%~90%的灵敏度和70%~90%的特异度，显著优于CXR的灵敏度（53%）和特异度（78%）。对于气胸，LUS检查的诊断特异度接近100%，且能进行半定量分析。LUS在重症患者的治疗监测中展现出广泛的应用价值。在机械通气患者的肺通气变化评估中，LUS能通过检测“A线”到“B线”再到“组织样征”的转变来评估肺复张情况，且LUS评分与计算机断层扫描(computed tomography, CT)结果高度相关(Rho=0.85，P<0.01)。重症患者的容量管理中，可使用LUS检测“B线”来评估液体超负荷情况，与超声心动图联合使用时，可帮助判断患者是否具有容量反应性。对于呼吸机相关性肺炎(ventilator-associated pneumonia, VAP)和急性呼吸窘迫综合征(acute respiratory distress syndrome, ARDS)，LUS不仅有助于诊断，还能评估病情的严重度及疗效。LUS的应用目前仍面临操作者技术的挑战，且存在一定局限性，包括操作者经验的依赖、肺部深度探测的限制、肥胖或胸部术后患者的LUS图像质量较差等问题。未来，结合人工智能技术有望进一步提升LUS的诊断准确率和效率。

Lung ultrasound (LUS) has important application value in the diagnosis and treatment of patients in intensive care units because of its advantages of non-radiation, low cost, real-time imaging, bedside operability, and no risk of patient transport. LUS utilizes B-mode and M-mode imaging to assess pulmonary lesions, with LUS scoring used to quantify “B-lines” and evaluate the severity of lesions, providing a systematic and objective lesion evaluation system. Since 2008, the bedside lung ultrasonography in emergency (BLUE) protocol and its subsequent modified protocol, bedside lung ultrasonography in emergency -plus (BLUE-plus), introduced by French critical care physician Dr. Lichtenstein, have achieved significant success in assessing acute respiratory failure. The BLUE protocol distinguishes causes of dyspnea based on various ultrasound patterns, with a sensitivity of 97% and specificity of 95% for diagnosing cardiogenic pulmonary edema, and a sensitivity of 89% and specificity of 94% for diagnosing pneumonia. The BLUE-plus protocol, which includes scans of the posterior region, increases the sensitivity for diagnosing atelectasis and pulmonary consolidation to 95.71%, with a specificity of 87.50%. With indicators such as “pleural sliding” and “B-lines” in LUS, the sensitivity for diagnosing pulmonary edema can achieve 96%, far surpassing the 65% sensitivity of chest X-ray (CXR). Additionally, LUS can differentiate between cardiogenic and non-cardiogenic pulmonary edema. LUS shows a sensitivity of 92% for diagnosing pleural effusion and can estimate the volume of fluid. For lung consolidation, LUS shows a sensitivity of 80% to 90% and a specificity of 70% to 90%, significantly outperforming CXR, which has a sensitivity of 53% and a specificity of 78%. For pneumothorax, the diagnostic specificity of LUS is nearly 100%, and it allows semi-quantitative analysis. LUS demonstrates broad applications in treatment monitoring for critically ill patients. In the assessment of lung ventilation changes in mechanically ventilated patients, LUS can evaluate lung recruitment by detecting the transitions from “A-lines” to “B-lines” and then to “alveolar-interstitial patterns”. The LUS scores are highly correlated with computed tomography (CT) results (Rho=0.85, P<0.01). In volume management for critically ill patients, LUS can assess fluid overload by detecting “B-lines”. When combined with echocardiography, it can help determine whether the patient has volume responsiveness. For ventilator-associated pneumonia (VAP) and acute respiratory distress syndrome (ARDS), LUS not only assists in diagnosis but also in evaluating disease severity and treatment efficacy. However, the application of LUS still faces challenges related to operator skills and has certain limitations, including dependence on operator experience, limited depth of lung detection, and poor image quality in obese or post-thoracic surgery patients. Future integration with artificial intelligence technology is expected to further enhance the diagnostic accuracy and efficiency of LUS.