Point-of-care ultrasound to confirm the position of bronchial blockers in children.

PURPOSE: We described the accuracy of ultrasound in determining the position of bronchial blockers (BBs) in children underwent thoracoscopic surgery. METHODS: We enrolled 52 children with ASA grade I-III who received thoracoscopic surgery with placement of BBs. Point-of-care ultrasound was performed according to the BLUE protocol. The ultrasound-guided lung sliding sign and curtain sign were used to assess the position of BBs. The accuracy of ultrasound in evaluating the position of BBs, as well as the accuracy and operating time of sliding sign and curtain sign at each examination point were recorded and compared. RESULTS: The accuracy of ultrasound in evaluating the position of BBs was 88% (46/52, 95% CI 0.69-0.97). When using the curtain sign to assess the position of BBs, the accuracy was 90% (94/104, 95% CI 0.78-0.96), which was significantly higher than when using the sliding sign (65% (136/208), 95% CI 0.55-0.74) (p = 0.002). The accuracy of curtain sign at the left mid-axillary line-diaphragm and the right mid-axillary line-diaphragm was respectively 96% (50/52, 95% CI 0.80-0.99) and 84% (44/52, 95% CI 0.65-0.95), which were higher than that of sliding sign at upper blue points and lower blue points. There was no significant difference in the operating time between two ultrasound signs (the curtain sign, 13.4 ± 8.2 s vs. the lung sliding sign, 16.2 ± 10.0 s, p = 0.065). CONCLUSION: Point-of-care ultrasound can effectively assess the position of BBs. The accuracy of using the curtain sign at the mid-axillary line-diaphragm is higher than that of using the lung sliding sign at the anterior chest wall.

Use of Ultrasonography for Lung Transplant Recipients on Postoperative Care.

The authors report their findings regarding lung ultrasound profiles in a population of transplant recipients. Twenty-two patients were studied once each in multiple different ultrasound windows focusing on pleural, lung, and diaphragmatic signatures. All studies were performed in presumably otherwise healthy recipients at an outpatient follow-up visit at least 3 months after transplantation. Those with recent pulmonary infections or decline in lung function were excluded from enrollment. The majority of scans revealed otherwise normal lungs with lung sliding, but there were more abnormalities than one would expect in a healthy control group. Lung ultrasonography will likely never replace other cross-sectional imaging given its inherent visual limitations but adds another modality to interrogate the lung/pleural interface and diaphragmatic function.

Utility of Lung Ultrasonography for Detection of Pleural Adhesions in Dogs.

OBJECTIVES: To assess lung respiratory movement ("lung sliding") in dogs using B-mode ultrasonography (US) and to develop a method that assesses adhesions between the parietal pleura and the lung. METHODS: Seventeen male beagles were anesthetized, and respiratory management was performed with intermittent positive pressure ventilation. Lung-sliding assessments and adhesion examinations were performed with lung US under general anesthesia before and 2 weeks after thoracotomy. Lung sliding was scored on a 4-level scale based on the percentage of the area that showed lung sliding (3, an area of roughly ≥80% of the intercostal space; 2, about 50% of the area of the intercostal space; 1, a small area of the intercostal space; or 0, movement absent); scores of 0, 1, and 2 indicated adhesions, whereas a score of 3 indicated no adhesions. The animals were then euthanized, and necropsy was performed to examine pleural adhesions. RESULTS: Lung US and necropsy findings were compared. The median lung-sliding score for the 12 sites with pleural adhesions on necropsy was 1.5, whereas it was 3.0 for the 532 sites without pleural adhesions. The lung-sliding score was significantly lower in the group with adhesions (P < .0001). Adhesion sites detected on necropsy were in accordance with the sites that had decreased lung-sliding scores. Lung US could detect pleural adhesions with sensitivity of 100.0% and specificity of 87.8%. CONCLUSIONS: Examination of lung sliding by thoracic US has high diagnostic value for detecting canine pleural adhesions and is useful in predicting adhesion sites before thoracic surgery in healthy dogs.

A novel method of assessing for lung sliding using Doppler imaging.

Ultrasound is an ideal modality in the emergency department (ED) to assess for pneumothorax given its rapid availability, portability, and repeatability to assess clinical status changes. Certain patient populations and clinical circumstances may present challenges to the performance of this examination. In this article, we review patterns of the presence or absence of lung sliding in the commonly utilized sonographic modes in the ED setting. We also describe a novel technique to evaluate lung sliding using tissue Doppler.

Lung ultrasonography to diagnose pneumothorax of the newborn.

OBJECTIVE: To explore the reliability and accuracy of lung ultrasound for diagnosing neonatal pneumothorax. METHODS: This study was divided into two phases. (1) In the first phase, from January 2013 to June 2015, 40 patients with confirmed pneumothorax had lung ultrasound examinations performed to identify the sonographic characteristics of neonatal pneumothorax. (2) In the second phase, from July 2015 to August 2016, lung ultrasound was undertaken on 50 newborn infants with severe lung disease who were suspected of having pneumothorax, to evaluate the sonographic accuracy and reliability to diagnose pneumothorax. RESULTS: (1) The main ultrasonic manifestations of pneumothorax are as follows: ① lung sliding disappearance, which was observed in all patients (100%); ② the existence of the pleural line and the A-line, which was also observed in all patients (100%); ③ the lung point, which was found in 75% of the infants with mild-moderate pneumothorax but not found to exist in 25% of the severe pneumothorax patients; ④ the absence of B-lines in the area of the pneumothorax (100% of the pneumothorax patients); and ⑤ no lung consolidation existed in the area of the pneumothorax (100% of the pneumothorax patients). (2) The accuracy and reliability of the lung sonographic signs of lung sliding disappearance as well as the existence of the pleural line and the A-line in diagnosing pneumothorax were as follows: 100% sensitivity, 100% specificity, 100% positive predictive value, and 100% negative predictive value. When the lung point exists, the diagnosis is mild-moderate pneumothorax, whereas if no lung point exists, the diagnosis is severe pneumothorax. CONCLUSION: Lung ultrasound is accurate and reliable in diagnosing and ruling out neonatal pneumothorax and, in our study, was found to be as accurate as chest X-ray.

Lung Sliding Identification Is Less Accurate in the Left Hemithorax.

OBJECTIVES: The aim of our study was to compare the accuracy of lung sliding identification for the left and right hemithoraxes, using prerecorded short US sequences, in a group of physicians with mixed clinical and US training. METHODS: A total of 140 US sequences of a complete respiratory cycle were recorded in the operating room. Each sequence was divided in two, yielding 140 sequences of present lung sliding and 140 sequences of absent lung sliding. Of these 280 sequences, 40 were randomly repeated to assess intraobserver variability, for a total of 320 sequences. Descriptive data, the mean accuracy of each participant, as well as the rate of correct answers for each of the original 280 sequences were tabulated and compared for different subgroups of clinical and US training. A video with examples of present and absent lung sliding and a lung pulse was shown before testing. RESULTS: Two sessions were planned to facilitate the participation of 75 clinicians. In the first group, the rate of accurate lung sliding identification was lower in the left hemithorax than in the right (67.0% [interquartile range (IQR), 43.0-83.0] versus 80.0% [IQR, 57.0-95.0]; P < .001). In the second group, the rate of accurate lung sliding identification was also lower in the left hemithorax than in the right (76.3% [IQR, 42.9-90.9] versus 88.7% [IQR, 63.1-96.9]; P = .001). Mean accuracy rates were 67.5% (95% confidence interval, 65.7-69.4) in the first group and 73.1% (95% confidence interval, 70.7-75.5) in the second (P < .001). CONCLUSIONS: Lung sliding identification seems less accurate in the left hemithorax when using a short US examination. This study was done on recorded US sequences and should be repeated in a live clinical situation to confirm our results.

Automated Real-Time Detection of Lung Sliding Using Artificial Intelligence: A Prospective Diagnostic Accuracy Study.

BACKGROUND: Rapid evaluation for pneumothorax is a common clinical priority. Although lung ultrasound (LUS) often is used to assess for pneumothorax, its diagnostic accuracy varies based on patient and provider factors. To enhance the performance of LUS for pulmonary pathologic features, artificial intelligence (AI)-assisted imaging has been adopted; however, the diagnostic accuracy of AI-assisted LUS (AI-LUS) deployed in real time to diagnose pneumothorax remains unknown. RESEARCH QUESTION: In patients with suspected pneumothorax, what is the real-time diagnostic accuracy of AI-LUS to recognize the absence of lung sliding? STUDY DESIGN AND METHODS: We performed a prospective AI-assisted diagnostic accuracy study of AI-LUS to recognize the absence of lung sliding in a convenience sample of patients with suspected pneumothorax. After calibrating the model parameters and imaging settings for bedside deployment, we prospectively evaluated its diagnostic accuracy for lung sliding compared with a reference standard of expert consensus. RESULTS: Two hundred forty-one lung sliding evaluations were derived from 62 patients. AI-LUS showed a sensitivity of 0.921 (95% CI, 0.792-0.973), specificity of 0.802 (95% CI, 0.735-0.856), area under the receiver operating characteristic curve of 0.885 (95% CI, 0.828-0.956), and accuracy of 0.824 (95% CI, 0.766-0.870) for the diagnosis of absent lung sliding. INTERPRETATION: In this study, real-time AI-LUS showed high sensitivity and moderate specificity to identify the absence of lung sliding. Further research to improve model performance and optimize the integration of AI-LUS into existing diagnostic pathways is warranted.

Diagnostic value of pulsed wave doppler in pneumothorax: a prospective study.

BACKGROUND: The diagnosis of pneumothorax is usually made through clinical examination and radiography. Pulsed wave (PW) Doppler mode has not previously been used in the diagnosis of pneumothorax on chest USG. AIMS: The aim of this study is to present, for the first time, a new finding demonstrating pleural movements using PW Doppler mode and to examine the value of the new sonographic finding in the diagnosis of pneumothorax. METHODS: We investigated the presence of PW artifact in patients with and without pneumothorax using the high-frequency probe in PW Doppler. The Dogan's sign, defined as the absence of PW artifact, was then compared with lung sliding and the barcode sign in pulsed wave Doppler for the diagnosis of pneumothorax. RESULTS: Of the 141 patients, 39 were in the pneumothorax group. The sensitivity and specificity of the Dogan's sign in the diagnosis of pneumothorax were 95.12% and 99.3%, respectively, in this study. The sensitivity and specificity of lung sliding were 95.12% and 98.08%, respectively; the sensitivity and specificity of the barcode sign were 92.86% and 98.08%, respectively, in the diagnosis of pneumothorax by ultrasonography in this study. CONCLUSION: PW Doppler is a useful tool in the diagnosis of pneumothorax. It has a high sensitivity and specificity for the detection of pneumothorax. It is also superior to both lung sliding and the barcode sign in detecting pneumothorax. The Dogan's sign can be used safely in the diagnosis of pneumothorax, together with lung sliding and the barcode sign.

Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification.

Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data. As annotated LUS data are relatively scarce-compared to other medical imaging data-we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model's performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data.

An Interpretable Neonatal Lung Ultrasound Feature Extraction and Lung Sliding Detection System Using Object Detectors.

The objective of this study was to develop an interpretable system that could detect specific lung features in neonates. A challenging aspect of this work was that normal lungs showed the same visual features (as that of Pneumothorax (PTX)). M-mode is typically necessary to differentiate between the two cases, but its generation in clinics is time-consuming and requires expertise for interpretation, which remains limited. Therefore, our system automates M-mode generation by extracting Regions of Interest (ROIs) without human in the loop. Object detection models such as faster Region Based Convolutional Neural Network (fRCNN) and RetinaNet models were employed to detect seven common Lung Ultrasound (LUS) features. fRCNN predictions were then stored and further used to generate M-modes. Beyond static feature extraction, we used a Hough transform based statistical method to detect "lung sliding" in these M-modes. Results showed that fRCNN achieved a greater mean Average Precision (mAP) of 86.57% (Intersection-over-Union (IoU) = 0.2) than RetinaNet, which only displayed a mAP of 61.15%. The calculated accuracy for the generated RoIs was 97.59% for Normal videos and 96.37% for PTX videos. Using this system, we successfully classified 5 PTX and 6 Normal video cases with 100% accuracy. Automating the process of detecting seven prominent LUS features addresses the time-consuming manual evaluation of Lung ultrasound in a fast paced environment. Clinical impact: Our research work provides a significant clinical impact as it provides a more accurate and efficient method for diagnosing lung diseases in neonates.

Determinants of point-of-care ultrasound lung sliding amplitude in mechanically ventilated patients.

BACKGROUND: Although lung sliding seen by point-of-care ultrasound (POCUS) is known to be affected to varying degrees by different physiologic and pathologic processes, it is typically only reported qualitatively in the critical care setting. Lung sliding amplitude quantitatively expresses the amount of pleural movement seen by POCUS but its determinants in mechanically ventilated patients are largely unknown. METHODS: This was a single-center, prospective, observational pilot study examining 40 hemithoraces in 20 adult patients receiving mechanical ventilation. Each subject had lung sliding amplitude measured in both B-mode and by pulsed wave Doppler at their bilateral lung apices and bases. Differences in lung sliding amplitude were correlated with anatomical location (apex vs base) as well as physiologic parameters including positive end expiratory pressure (PEEP), driving pressure, tidal volume and the ratio of arterial partial pressure of oxygen (PaO2) to fraction of inspired oxygen (FiO2). RESULTS: POCUS lung sliding amplitude was significantly lower at the lung apex compared to the lung base in both B-mode (3.6 ± 2.0 mm vs 8.6 ± 4.3 mm; p < 0.001) and the pulsed wave Doppler mode (10.3 ± 4.6 cm/s vs 13.9 ± 5.5 cm/s; p < 0.001) corresponding to expected distribution of ventilation to the lung bases. Inter-rater reliability of B-mode measurements was excellent (ICC = 0.91) and distance traversed in B-mode had a significant positive correlation with pleural line velocity (r2 = 0.32; p < 0.001). There was a non-statistically significant trend towards lower lung sliding amplitude for PEEP ≥ 10 cmH2O, as well as for driving pressure ≥ 15 cmH2O in both ultrasound modes. CONCLUSION: POCUS lung sliding amplitude was significantly lower at the lung apex than the lung base in mechanically ventilated patients. This was true when using both B-mode and pulsed wave Doppler. Lung sliding amplitude did not correlate with PEEP, driving pressure, tidal volume or PaO2:FiO2 ratio. Our findings suggest that lung sliding amplitude can be quantified in mechanically ventilated patients in a physiologically predictable way and with high inter-rater reliability. A better understanding of POCUS derived lung sliding amplitude and its determinants may aid in the more accurate diagnosis of lung pathologies, including pneumothorax, and could serve as a means of further reducing radiation exposure and improving outcomes in critically ill patients.

Point-of-care AI-assisted stepwise ultrasound pneumothorax diagnosis.

Objective. Ultrasound is extensively utilized as a convenient and cost-effective method in emergency situations. Unfortunately, the limited availability of skilled clinicians in emergency hinders the wider adoption of point-of-care ultrasound. To overcome this challenge, this paper aims to aid less experienced healthcare providers in emergency lung ultrasound scans.Approach. To assist healthcare providers, it is important to have a comprehensive model that can automatically guide the entire process of lung ultrasound based on the clinician's workflow. In this paper, we propose a framework for diagnosing pneumothorax using artificial intelligence (AI) assistance. Specifically, the proposed framework for lung ultrasound scan follows the steps taken by skilled physicians. It begins with finding the appropriate transducer position on the chest to locate the pleural line accurately in B-mode. The next step involves acquiring temporal M-mode data to determine the presence of lung sliding, a crucial indicator for pneumothorax. To mimic the sequential process of clinicians, two DL models were developed. The first model focuses on quality assurance (QA) and regression of the pleural line region-of-interest, while the second model classifies lung sliding. To achieve the inference on a mobile device, a size of EfficientNet-Lite0 model was further reduced to have fewer than 3 million parameters.Main results. The results showed that both the QA and lung sliding classification models achieved over 95% in area under the receiver operating characteristic (AUC), while the ROI performance reached 89% in the dice similarity coefficient. The entire stepwise pipeline was simulated using retrospective data, yielding an AUC of 89%.Significance. The step-wise AI framework for the pneumothorax diagnosis with QA offers an intelligible guide for each clinical workflow, which achieved significantly high precision and real-time inferences.

Early detection of delayed pneumothorax using lung ultrasound after transthoracic needle lung biopsy: A prospective pilot study.

OBJECTIVES: Delayed pneumothorax can cause an emergency room visit and be life-threatening in case of tension pneumothorax after transthoracic needle biopsy. We hypothesized that most delayed pneumothoraces are diagnosed by later enlargement of occult pneumothorax due to the low diagnostic accuracy of a chest X-ray. Lung ultrasound is a highly accurate tool for detection of pneumothorax. The aim of this study is to evaluate the diagnostic accuracy of lung ultrasound for prediction of delayed pneumothorax on chest X-ray. METHODS: This prospective pilot study was performed between April 2020 and July 2020 in Chungnam National University Hospital. The participants underwent chest X-rays and lung ultrasound before, immediately after, and 3 h after transthoracic needle biopsy, respectively. The presence or absence of lung sliding at each anterior BLUE-point on an ultrasound and pneumothorax on a chest X-ray was recorded. RESULTS: Pneumothorax occurred in 17 (35.4%) participants, and three of them underwent chest tube replacement. Of the 17 (35.4%) cases of pneumothorax, five participants (10.4%) were diagnosed with delayed pneumothorax. Three out of five participants showed loss of lung sliding on lung ultrasound before the diagnosis of delayed pneumothorax. Therefore, the sensitivity of lung sliding on lung ultrasound for early detection of delayed pneumothorax was 60%. Two undetected cases were asymptomatic, and the pneumothoraces were exceedingly small and recovered spontaneously. Thus, sensitivity for detection of clinically meaningful delayed pneumothorax requiring chest tube replacement was 100% (2/2). CONCLUSION: Lung ultrasound can probably predict clinically meaningful delayed pneumothorax after transthoracic needle lung biopsy.

Lung Ultrasound to Assess One Lung Ventilation: A Pediatric Case Series.

Objectives: One lung ventilation (OLV) is the preferred ventilation technique for thoracoscopy as it provides a better exposure of the operative field and grants the protection of the healthy lung. Preoperative evaluation of lung exclusion is necessary and different methods are available. In recent years lung ultrasound (US) gained popularity and its use for monitoring the endotracheal tube position is widely reported. The existing evidence on adults addresses lung US as effective, yet only few data are available in children. Therefore, we present our experience with lung US as verification method for pediatric OLV. Methods: All patients undergoing OLV for video-assisted thoracoscopic surgery from January 2019 to May 2021 and for whom lung exclusion was confirmed through lung US were involved. Lung exclusion was considered effective when absence of lung motion and presence of lung pulse were encountered. When lung US did not match these criteria, repositioning of the endobronchial device followed by US verification was performed. When lung US met the exclusion criteria surgery was started and direct thoracoscopic observation was used to verify lung exclusion. Results: A total of 20 patients, accounting for 22 procedures, were involved. Absence of lung motion and presence of lung pulse were assessed in the operative-side lung for all patients. Lung exclusion was confirmed through thoracoscopy. Postoperative lung US proved the reappearance of lung motion in the previously excluded lung. Conclusions: In our center experience lung US resulted to be a safe, effective, and time-saving verification method for OLV. Further studies are needed to define its sensitivity and specificity.

Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach.

Pneumothorax is a potentially life-threatening condition that can be rapidly and accurately assessed via the lung sliding artefact generated using lung ultrasound (LUS). Access to LUS is challenged by user dependence and shortage of training. Image classification using deep learning methods can automate interpretation in LUS and has not been thoroughly studied for lung sliding. Using a labelled LUS dataset from 2 academic hospitals, clinical B-mode (also known as brightness or two-dimensional mode) videos featuring both presence and absence of lung sliding were transformed into motion (M) mode images. These images were subsequently used to train a deep neural network binary classifier that was evaluated using a holdout set comprising 15% of the total data. Grad-CAM explanations were examined. Our binary classifier using the EfficientNetB0 architecture was trained using 2535 LUS clips from 614 patients. When evaluated on a test set of data uninvolved in training (540 clips from 124 patients), the model performed with a sensitivity of 93.5%, specificity of 87.3% and an area under the receiver operating characteristic curve (AUC) of 0.973. Grad-CAM explanations confirmed the model's focus on relevant regions on M-mode images. Our solution accurately distinguishes between the presence and absence of lung sliding artefacts on LUS.

Tissue-specific deformable image registration using a spatial-contextual filter.

Intensity-based deformable registration with spatial-invariant regularization generally fails when distinct motion exists across different types of tissues. The purpose of this work was to develop and validate a new regularization approach for deformable image registration that is tissue-specific and able to handle motion discontinuities. Our approach was built upon a Demons registration framework, and used the image context supplementing the original spatial constraint to regularize displacement vector fields in iterative image registration process. The new regularization was implemented as a spatial-contextual filter, which favors the motion vectors within the same tissue type but penalizes the motion vectors from different tissues. This approach was validated using five public lung cancer patients, each with 300 landmark pairs identified by a thoracic radiation oncologist. The mean and standard deviation of the landmark registration errors were 1.3 ± 0.8 mm, compared with those of 2.3 ± 2.9 mm using the original Demons algorithm. Particularly, for the case with the largest initial landmark displacement of 15 ± 9 mm, the modified Demons algorithm had a registration error of 1.3 ± 1.1 mm, while the original Demons algorithm had a registration error of 3.6 ± 5.9 mm. We also qualitatively evaluated the modified Demons algorithm using two difficult cases in our routine clinic: one lung case with large sliding motion and one head and neck case with large anatomical changes in air cavity. Visual evaluation on the deformed image created by the deformable image registration showed that the modified Demons algorithm achieved reasonable registration accuracy, but the original Demons algorithm produced distinct registration errors.

Lung Ultrasonography for Pneumothorax in Dogs and Cats.

A sonographic diagnosis of pneumothorax (PTX) traditionally relies on excluding the presence of lung sliding, lung pulse, and/or B lines/lung consolidations, and identifying the lung point. However, these criteria can be difficult to identify, particularly in critically ill patients with respiratory disorders, and the lung point is infrequently used. Newer sonographic findings, such as mirrored ribs, reverse lung sliding, and abnormal curtain signs, have been identified to try to increase the accuracy of diagnosing PTX. This article describes and discusses the lung ultrasonography criteria used to diagnose PTX in both human and small animal patients.

Speckle tracking quantification of lung sliding for the diagnosis of pneumothorax: a multicentric observational study.

PURPOSE: Lung ultrasound is used for the diagnosis of pneumothorax, based on lung sliding abolition which is a qualitative and operator-dependent assessment. Speckle tracking allows the quantification of structure deformation over time by analysing acoustic markers. We aimed to test the ability of speckle tracking technology to quantify lung sliding in a selected cohort of patients and to observe how the technology may help the process of pneumothorax diagnosis. METHODS: We performed retrospectively a pleural speckle tracking analysis on ultrasound loops from patients with pneumothorax. We compared the values measured by two observers from pneumothorax side with contralateral normal lung side. The receiver operating characteristic (ROC) curve was constructed to evaluate the performance of maximal pleural strain to detect the lung sliding abolition. Diagnosis performance and time to diagnosis between B-Mode and speckle tracking technology were compared from a third blinded observer. RESULTS: We analysed 104 ultrasound loops from 52 patients. The area under the ROC curve of the maximal pleural strain value to identify lung sliding abolition was 1.00 [95%CI 1.00; 1.00]. Specificity was 100% [95%CI 93%; 100%] and sensitivity was 100% [95%CI 93%; 100%] with the best cut-off of 4%. Over 104 ultrasound loops, the blinded observer made two errors with B-Mode and none with speckle tracking. The median diagnosis time was 3 [2-5] seconds for B-Mode versus 2 [1-2] seconds for speckle tracking (p = 0.001). CONCLUSION: Speckle tracking technology allows lung sliding quantification and detection of lung sliding abolition in case of pneumothorax on selected ultrasound loops.

Lung Ultrasound (in the Critically Ill) Superior to CT: the Example of Lung Sliding.

This review article shows the potential of lung ultrasound in the critically ill (LUCI) to study lung sliding and describes the optimal equipment for its assessment. Then, it analyses the integration of lung sliding within lung ultrasound then whole body critical ultrasound. It describes the place of lung sliding in the BLUE-protocol (bedside lung ultrasound in emergency) (lung and venous ultrasound for diagnosing acute respiratory failure), the FALLS-protocol (fluid administration limited by lung sonography) (the role of lung sliding in circulatory failure), and the SESAME-protocol (sequential assessment of sonography assessing mechanism or origin of severe shock of indistinct cause) (whole body ultrasound in cardiac arrest). In the LUCIFLR project (LUCI favoring limitation of radiations), the consideration of lung sliding allows drastic reduction in irradiation and costs. In conclusion, lung sliding is proposed as a gold standard for indicating the presence of the lung at the chest wall and its correct expansion.

Speckle tracking technology for quantifying lung sliding.

Ultrasound (US) is gaining recognition as a useful tool for assessing lung physiology and pathology. Yet, currently the skill of performing lung US is taught by experienced operators to novice ones, mainly by recognizing expected patterns. Recognizing the latter may be difficult and subjective. In this hypothesis we propose to apply a well-known and used image processing technology in echocardiography, speckle tracking (ST), to lung sliding - the marker of normal lung function. If implementing ST to lung sliding is technically feasible, several outcomes are expected: (1) Lung sliding will become an objective, operator-independent marker of normal lung function. (2) Subsequently, ST will provide normal values for lung sliding. (3) Lastly, the effects of pulmonary pathologies on lung sliding may be assessed. It is stressed, however, that the preliminary idea suggested here is limited to a single physiological phenomenon (lung sliding). Only when technical feasibility is demonstrated then ST technology may potentially be applied and investigated in other clinical settings of lung diseases.