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Background: Lung ultrasound can evaluate for pneumothorax but the accuracy of diagnosis depends on experience among physicians. This study aimed to investigate the sensitivity and specificity of intelligent lung ultrasound in comparison with chest x‐ray, employing chest computed tomography (CT) as the gold standard for diagnosis of pneumothorax in critical ill patients. Methods: This prospective, observational study included 75 dyspnea patients admitted to the Intensive Care Unit of the Fourth Affiliated Hospital of Soochow University from January 2021 to April 2023. Lung ultrasound images were collected using BLUE‐plus protocol and analyzed by artificial intelligence software to identify the pleural line, with CT results serving as the gold standard for diagnosis. Pneumothorax was diagnosed based on either the disappearance of pleural slip sign or identification of lung point. Additionally, chest x‐ray images and diagnostic results were also obtained during the same period for comparison. Results: The sensitivity and specificity of intelligent lung ultrasound in diagnosing pneumothorax were 79.4% and 85.4%, respectively. The sensitivity and specificity of x‐ray diagnosis were 82.4% and 80.5%. Additionally, the diagnostic time for lung ultrasound was significantly shorter than that for x‐ray examination. Conclusion: Intelligent lung ultrasound has diagnostic efficiency comparable to that of x‐ray examination but offers advantages in terms of speed. [ABSTRACT FROM AUTHOR]

Background: Lung ultrasonography (LUS) is a valuable diagnostic tool, but there is a shortage of LUS experts with extensive knowledge and significant experience in the field. Convolutional neural networks (CNNs) have the potential to mitigate this issue by facilitating computer-aided diagnosis. Methods: We propose computer-aided system by a CNN-based method for LUS diagnosis. As the first consideration, we investigated pleural line and lung sliding. The pleural line indicates the position of pleura in an ultrasound image, and LUS is performed after first confirming the position of pleural line. Lung sliding defined as the movement of the pleural line, and the absence of this feature is associated with pneumothorax. Results: Our proposed method accurately detected pleural line and lung sliding, demonstrating its potential to provide valuable diagnostic information on lung lesions.

The aim of the work described here was to determine the effects of imaging protocol, technique and pleural line depth on measured pleural line thickness (PLT). Sonograms were performed on a phantom and healthy volunteers. In vitro , pleural line depth, transducer type (5–1 MHz phased array vs. 13–6 MHz linear array), angle of the pleural line relative to the transducer and distance between the pleural line and focal length were explicitly modified. PLT was measured using electronic calipers. Regression equations described the effects of independent variables on PLT. Factors influencing PLT in vitr o were tested in vivo. In vitro (n = 250 sonograms), PLT was 3.8 (standard error: ±0.24) mm greater when using the phased array compared with the linear transducer (p < 0.001). For every additional centimeter of pleural line depth, PLT increased by 0.96 (±0.081) mm for the phased array (p < 0.001) and 0.26 (±0.019) mm for the linear transducer (p < 0.001). Neither pleural angle nor focal length altered PLT. In vivo (n = 160 sonograms), PLT was 2.56 (±0.06) mm greater when using the phased array (p < 0.001) compared with the linear transducer. PLT increased by 0.67 (±0.060) mm with the phased array (p < 0.001) and 0.25 mm (±0.019) with the linear transducer (p < 0.001) for every additional centimeter between the transducer and the pleura. Together the variables explained 93% of PLT variance in vivo (p < 0.001). PLT measurements are affected by transducer type and pleural line depth. Future studies evaluating PLT as a disease marker should account for confounding by these variables. [ABSTRACT FROM AUTHOR]

Background: Lung ultrasonography (LUS) is a valuable diagnostic tool, but there is a shortage of LUS experts with extensive knowledge and significant experience in the field. Convolutional neural networks (CNNs) have the potential to mitigate this issue by facilitating computer-aided diagnosis., Methods: We propose computer-aided system by a CNN-based method for LUS diagnosis. As the first consideration, we investigated pleural line and lung sliding. The pleural line indicates the position of pleura in an ultrasound image, and LUS is performed after first confirming the position of pleural line. Lung sliding defined as the movement of the pleural line, and the absence of this feature is associated with pneumothorax., Results: Our proposed method accurately detected pleural line and lung sliding, demonstrating its potential to provide valuable diagnostic information on lung lesions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Objectives: Over the last years ultrasound has shown to be an important tool for evaluating lung involvement, including interstitial lung disease (ILD) a potentially severe systemic involvement in many rheumatic and musculoskeletal diseases (RMD). Despite the potential sensitivity of the technique the actual use is hampered by the lack of consensual definitions of elementary lesions to be assessed and of the scanning protocol to apply. Within the Outcome Measures in Rheumatology (OMERACT) Ultrasound Working Group we aimed at developing consensus-based definitions for ultrasound detected ILD findings in RMDs and assessing their reliability in dynamic images., Methods: Based on the results from a systematic literature review, several findings were identified for defining the presence of ILD by ultrasound (i.e., Am-lines, B-lines, pleural cysts and pleural line irregularity). Therefore, a Delphi survey was conducted among 23 experts in sonography to agree on which findings should be included and on their definitions. Subsequently, a web-reliability exercise was performed to test the reliability of the agreed definitions on video-clips, by using kappa statistics., Results: After three rounds of Delphi an agreement >75 % was obtained to include and define B-lines and pleural line irregularity as elementary lesions to assess. The reliability in the web-based exercise, consisting of 80 video-clips (30 for pleural line irregularity, 50 for B-lines), showed moderate inter-reader reliability for both B-lines (kappa = 0.51) and pleural line irregularity (kappa = 0.58), while intra-reader reliability was good for both B-lines (kappa = 0.72) and pleural line irregularity (kappa = 0.75)., Conclusion: Consensus-based ultrasound definitions for B-lines and pleural line irregularity were obtained, with moderate to good reliability to detect these lesions using video-clips. The next step will be testing the reliability in patients with ILD linked to RMDs and to propose a consensual and standardized protocol to scan such patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)