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.
