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Background: Focused Assessment with Sonography in Trauma (FAST) examination is a point-of-care ultrasound study used to evaluate for abdominal hemorrhage, pneumothorax, or pericardial blood in trauma patients as an adjunct to their initial assessment. The quality of the image can be limited, and its diagnostic value is heavily dependent on operator skill. Our objective was to determine whether a standardized review process improved image quality and reduced incidence of nondiagnostic or insufficient imaging by 10% over a 6-month period. Study Design: Between July 1, 2021, and March 31, 2022, we evaluated 1106 trauma activations at our level II trauma center. Two exams per practitioner per month were reviewed by an emergency medicine trained traumatologist with specialized training in point-of-care ultrasound and board certification in echocardiography. Priority was given to exams on patients with known injuries identified on other studies. If there were no exams that matched these criteria, random exams were selected. Images were reviewed for image quality, diagnostic accuracy, and labeling with counseling given to the provider if indicated. Categorical variables were compared using chi squared analysis, while continuous non-normally distributed variables were compared using the Mann-Whitney U test. Results: A total of 305 FAST exams were reviewed (186 pre-intervention and 119 during intervention). Image quality improved from 46.3% (n = 31/65) to 79.0% (n = 94/119) ( P < .01) with need for counseling falling from 63.1% (n = 41/65) pre-QI to 42.0% (n = 50/119) post-QI ( P < .01). Incidence of detectable injury, BMI, ISS, and AIS body regions were consistent across the study period. This was seen in both the geriatric and non-geriatric cohorts despite a significant increase in ISS in the post-intervention geriatric patients. Discussion: A FAST review program is associated with improvement in image quality and decreased need for counseling of trauma providers., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: This study aimed to develop and assess the performance of an artificial intelligence (AI)-driven decision support system, XRAInet, in accurately identifying pediatric patients with pleural effusion or pneumothorax and determining whether tube thoracostomy intervention is warranted., Methods: In this diagnostic accuracy study, we retrospectively analyzed a data set containing 510 X-ray images from 170 pediatric patients admitted between 2005 and 2022. Patients were categorized into two groups: Tube (requiring tube thoracostomy) and Conservative (managed conservatively). XRAInet, a deep learning-based algorithm, was trained using this data set. We evaluated its performance using various metrics, including mean Average Precision (mAP), recall, precision, and F1 score., Results: XRAInet, achieved a mAP score of 0.918. This result underscores its ability to accurately identify and localize regions necessitating tube thoracostomy for pediatric patients with pneumothorax and pleural effusion. In an independent testing data set, the model exhibited a sensitivity of 64.00% and specificity of 96.15%., Conclusion: In conclusion, XRAInet presents a promising solution for improving the detection and decision-making process for cases of pneumothorax and pleural effusion in pediatric patients using X-ray images. These findings contribute to the expanding field of AI-driven medical imaging, with potential applications for enhancing patient outcomes. Future research endeavors should explore hybrid models, enhance interpretability, address data quality issues, and align with regulatory requirements to ensure the safe and effective deployment of XRAInet in healthcare settings., (© 2024 Wiley Periodicals LLC.)

Background: Spontaneous pneumothorax is a common clinical condition due to several etiologies. Appropriate radiologic assessment helps avoid re-imaging and guide individualized management., Objective: To describe the etiologic factors of spontaneous pneumothorax in children from a radiological perspective., Materials and Methods: Thirty-nine children with spontaneous pneumothorax were evaluated according to their demographic characteristics, the presence of underlying lung diseases, and imaging findings., Results: Twenty-one patients without underlying lung disease were assessed as primary spontaneous pneumothorax; eight of these 21 patients (38.9%) had subpleural air cysts in the apices/upper lobes of the lung on chest computed tomography (CT). In the remaining 18 patients with secondary spontaneous pneumothorax, the most common causes were respiratory diseases (33.3%), infectious lung diseases (33.3%), interstitial lung diseases (27.7%), and connective tissue diseases (5.5%). The mean age of children with secondary spontaneous pneumothorax was lower than that of children with primary spontaneous pneumothorax (P = 0.002). Recurrences occurred in 11 patients (52.3%) with primary spontaneous pneumothorax and three patients (16.6%) with secondary spontaneous pneumothorax. Bilateral pneumothorax was observed in three of the 18 patients with secondary spontaneous pneumothorax., Conclusion: Identifying the etiologic factors of spontaneous pneumothorax may help clinicians plan how to reduce the risk of recurrence. Radiologists should keep in mind the possible underlying diseases and assess imaging methods in children with spontaneous pneumothorax for subpleural air cysts and be suspicious about an underlying disease in cases of bilateral pneumothorax and in younger patients., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (© 2024 The Author(s). Journal of Clinical Ultrasound published by Wiley Periodicals LLC.)

Systemic air embolism is a rare but potentially life-threatening complication of computed tomography (CT)-guided lung biopsy. The largest lung biopsy audits report an incidence rate of approximately 0.061% for systemic air embolism, with a mortality rate of 0.07-0.15%. A prompt diagnosis with high index of suspicion is essential, and hyperbaric oxygen treatment (HBOT) is the definitive management. We report the case of a 44-year-old lady who developed a lateral ST elevation myocardial infarction from coronary artery air embolism following CT-guided lung biopsy for evaluation of a left lung lesion. The biopsy was performed in the right lateral decubitus position, and the patient reported chest pain after coughing during the procedure. The clinician decided to proceed, taking four biopsy samples as no pneumothorax was identified in the intraprocedural CT image. The patient was noted to have hypotension with ongoing chest pain post-procedure. Resuscitative measures were taken to stabilise her haemodynamics, and she was successfully treated with HBOT with total resolution of air embolism. She developed a left sided pneumothorax post-treatment and needed intercostal chest drain insertion. The left lung fully re-expanded, and the patient was discharged home after day two of admission., Competing Interests: The authors report no conflicts of interest in this work., (Copyright: This article is the copyright of the authors who grant Diving and Hyperbaric Medicine a non-exclusive licence to publish the article in electronic and other forms.)

Background: Artificial intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. Most research to date has focused on the performance of AI-assisted algorithms in comparison with that of radiologists rather than evaluating the algorithms' impact on the clinicians who often undertake initial image interpretation in routine clinical practice. This study assessed the impact of AI-assisted image interpretation on the diagnostic performance of frontline acute care clinicians for the detection of pneumothoraces (PTX)., Methods: A multicentre blinded multi-case multi-reader study was conducted between October 2021 and January 2022. The online study recruited 18 clinician readers from six different clinical specialties, with differing levels of seniority, across four English hospitals. The study included 395 plain CXR images, 189 positive for PTX and 206 negative. The reference standard was the consensus opinion of two thoracic radiologists with a third acting as arbitrator. General Electric Healthcare Critical Care Suite (GEHC CCS) PTX algorithm was applied to the final dataset. Readers individually interpreted the dataset without AI assistance, recording the presence or absence of a PTX and a confidence rating. Following a 'washout' period, this process was repeated including the AI output., Results: Analysis of the performance of the algorithm for detecting or ruling out a PTX revealed an overall AUROC of 0.939. Overall reader sensitivity increased by 11.4% (95% CI 4.8, 18.0, p=0.002) from 66.8% (95% CI 57.3, 76.2) unaided to 78.1% aided (95% CI 72.2, 84.0, p=0.002), specificity 93.9% (95% CI 90.9, 97.0) without AI to 95.8% (95% CI 93.7, 97.9, p=0.247). The junior reader subgroup showed the largest improvement at 21.7% (95% CI 10.9, 32.6), increasing from 56.0% (95% CI 37.7, 74.3) to 77.7% (95% CI 65.8, 89.7, p<0.01)., Conclusion: The study indicates that AI-assisted image interpretation significantly enhances the diagnostic accuracy of clinicians in detecting PTX, particularly benefiting less experienced practitioners. While overall interpretation time remained unchanged, the use of AI improved diagnostic confidence and sensitivity, especially among junior clinicians. These findings underscore the potential of AI to support less skilled clinicians in acute care settings., Competing Interests: Competing interests: AS, SG and AB are employed by GE HealthCare, a key NCIMI stakeholder. AN and CB have undertaken paid consultancy work for GEHC. PA, SA and FG are employees of Report and Image Quality Control (www.raiqc.com), a spin-out company from Oxford University Hospitals NHS Foundation Trust., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Routine chest X-ray (CXR) post intrathoracic drain removal in cardiac surgical patients is common practice to identify the presence of a pneumothorax following drain removal. Such pneumothoraces occur infrequently and rarely require intervention. We investigated the utility of routine CXR post drain removal and hypothesised that the practice is unnecessary and a possible area for significant cost saving. We conducted a single-centre, retrospective study of 390 patients who underwent cardiac surgery over a one-year period. Routine CXR post drain removal was reviewed for the presence of a pneumothorax. Rates of intervention post routine CXR were analysed to assess for clinical benefit obtained from this practice. Potential cost savings were calculated by the cost of a mobile CXR and by considering the radiographer's time. There were 15 pneumothoraces detected on routine CXR post drain removal. All pneumothoraces detected on routine post drain removal CXR were defined as small. No patients had a clinically significant pneumothorax requiring re-insertion of a chest drain. The potential cost saved by omitting routine CXR post drain removal was estimated to be approximately A$7750 per year. This study did not detect any clinically significant pneumothoraces requiring intervention. It also suggests that routine CXR post drain removal does not provide any clinical benefit and indicates that current practice should be reviewed.

Introduction: Speckle tracking technology quantifies lung sliding and detects lung sliding abolition in case of pneumothorax on selected ultrasound loops through the analysis of acoustic markers., Objectives: We aimed to test the ability of speckle tracking technology to quantify lung sliding using a pleural strain value (PS)., Methods: We performed a prospective study in 30 healthy volunteers in whom we assessed the pleural speckle tracking using ultrasound loops. Seven breathing conditions with and without non-invasive ventilation were tested. Two observers analyzed the ultrasound loops in four lung areas (anterior and posterior, left and right) and compared the obtained PS values. The first endpoint was to determine the feasibility of the PS measurement in different breathing conditions. The secondary endpoints were to assess the intra- and inter-observer's reliability of the measurement to compare PS values between anterior and posterior lung areas and to explore their correlations with the measured tidal volume., Results: We analyzed 1624 ultrasound loops from 29 patients after one volunteer's exclusion. Feasibility of this method was rated at 90.8 [95%CI: 89.6 - 92.4]%. The intra-observer reliability measured through Intraclass Correlation Coefficients was 0.96 [95%CI: 0.91-0.98] and 0.93 [95%CI: 0.86-0.97] depending on the operator. The inter-observer reliability was 0.89 [95%CI: 0.78-0.95]. The PS values were significantly lower in the anterior lung areas compared with the posterior areas in all breathing conditions. A weak positive correlation was found in all the lung areas when a positive end expiratory pressure was applied with r = 0.26 [95%CI: 0.12;0.39]; p < 0.01., Conclusion: Speckle tracking lung sliding quantification with PS was applicable in most conditions with an excellent intra- and inter-observer reliability. More studies in patients under invasive mechanical ventilation are needed to explore the correlation between PS values of pleural sliding and tidal volumes., Clinical Registration: NCT05415605., Competing Interests: Declaration of competing interest Laurent Müller and Laurent Zieleskiewcz received fees from General Electric Healthcare for teaching ultrasound to General Electric customers and for lecturing. Marc Leone received fees from LFB, Viatris and AOP pharma. Others authors do not have any conflict of interest to declare., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Differentiating between giant pulmonary bullae and pneumothorax can pose a challenge in clinical settings. A chest CT scan during the patient's health assessment revealed that approximately 40% of the right chest cavity was filled with air, leading to incomplete expansion of the right lung. The patient was initially misdiagnosed with pneumothorax in the emergency department and subsequently underwent closed thoracic drainage without experiencing any improvement in symptoms. Upon further examination through thoracoscopy, the patient was correctly diagnosed with a giant pulmonary bulla. Upon reviewing the patient's chest CT scan, we were able to identify key distinguishing features between giant pulmonary bullae and pneumothorax., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yefei Zhan reports financial support was provided by Ningbo No.2 Hospital (Project No. 2023-Y06). Yefei Zhan reports financial support was provided by Ningbo Municipal Health Commission (Project No. 2022-F16). If there are other authors, they 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 Elsevier Inc. All rights reserved.)

Purpose:  The aim of this study was to develop an algorithm to automatically extract annotations from German thoracic radiology reports to train deep learning-based chest X-ray classification models., Materials and Methods:  An automatic label extraction model for German thoracic radiology reports was designed based on the CheXpert architecture. The algorithm can extract labels for twelve common chest pathologies, the presence of support devices, and "no finding". For iterative improvements and to generate a ground truth, a web-based multi-reader annotation interface was created. With the proposed annotation interface, a radiologist annotated 1086 retrospectively collected radiology reports from 2020-2021 (data set 1). The effect of automatically extracted labels on chest radiograph classification performance was evaluated on an additional, in-house pneumothorax data set (data set 2), containing 6434 chest radiographs with corresponding reports, by comparing a DenseNet-121 model trained on extracted labels from the associated reports, image-based pneumothorax labels, and publicly available data, respectively., Results:  Comparing automated to manual labeling on data set 1: "mention extraction" class-wise F1 scores ranged from 0.8 to 0.995, the "negation detection" F1 scores from 0.624 to 0.981, and F1 scores for "uncertainty detection" from 0.353 to 0.725. Extracted pneumothorax labels on data set 2 had a sensitivity of 0.997 [95 % CI: 0.994, 0.999] and specificity of 0.991 [95 % CI: 0.988, 0.994]. The model trained on publicly available data achieved an area under the receiver operating curve (AUC) for pneumothorax classification of 0.728 [95 % CI: 0.694, 0.760], while the models trained on automatically extracted labels and on manual annotations achieved values of 0.858 [95 % CI: 0.832, 0.882] and 0.934 [95 % CI: 0.918, 0.949], respectively., Conclusion:  Automatic label extraction from German thoracic radiology reports is a promising substitute for manual labeling. By reducing the time required for data annotation, larger training data sets can be created, resulting in improved overall modeling performance. Our results demonstrated that a pneumothorax classifier trained on automatically extracted labels strongly outperformed the model trained on publicly available data, without the need for additional annotation time and performed competitively compared to manually labeled data., Key Points:   · An algorithm for automatic German thoracic radiology report annotation was developed.. · Automatic label extraction is a promising substitute for manual labeling.. · The classifier trained on extracted labels outperformed the model trained on publicly available data.., Zitierweise: · Wollek A, Hyska S, Sedlmeyr T et al. German CheXpert Chest X-ray Radiology Report Labeler. Fortschr Röntgenstr 2024; 196: 956 - 965., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

This report highlights the early and unusual detection of a pneumopericardium by echocardiography prior to potential development of cardiocirculatory compromise. It is important to consider pneumopericardium into the differential diagnosis when difficulties arise in the visualization of the heart by conventional echocardiography. Pneumopericardium is associated with a high mortality rate and may be effectively treated by immediate insertion of a pericardial catheter., (© 2023. The Author(s).)

Shape registration of patient-specific organ shapes to endoscopic camera images is expected to be a key to realizing image-guided surgery, and a variety of applications of machine learning methods have been considered. Because the number of training data available from clinical cases is limited, the use of synthetic images generated from a statistical deformation model has been attempted; however, the influence on estimation caused by the difference between synthetic images and real scenes is a problem. In this study, we propose a self-supervised offline learning framework for model-based registration using image features commonly obtained from synthetic images and real camera images. Because of the limited number of endoscopic images available for training, we use a synthetic image generated from the nonlinear deformation model that represents possible intraoperative pneumothorax deformations. In order to solve the difficulty in estimating deformed shapes and viewpoints from the common image features obtained from synthetic and real images, we attempted to improve the registration error by adding the shading and distance information that can be obtained as prior knowledge in the synthetic image. Shape registration with real camera images is performed by learning the task of predicting the differential model parameters between two synthetic images. The developed framework achieved registration accuracy with a mean absolute error of less than 10 mm and a mean distance of less than 5 mm in a thoracoscopic pulmonary cancer resection, confirming improved prediction accuracy compared with conventional methods., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Megumi Nakao report financial support was provided by the Japan Society for the Promotion of Science., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

This study evaluated the positive predictive value (PPV) of artificial intelligence (AI) in detecting pneumothorax on chest radiographs (CXRs) and its affecting factors. Patients determined to have pneumothorax on CXR by a commercial AI software from March to December 2021 were included retrospectively. The PPV was evaluated according to the true-positive (TP) and false-positive (FP) diagnosis determined by radiologists. To know the factors that might influence the results, logistic regression with generalized estimating equation was used. Among a total of 87,658 CXRs, 308 CXRs with 331 pneumothoraces from 283 patients were finally included. The overall PPV of AI about pneumothorax was 41.1% (TF:FP = 136:195). The PA view (odds ratio [OR], 29.837; 95% confidence interval [CI], 15.062-59.107), high abnormality score (OR, 1.081; 95% CI, 1.066-1.097), large amount of pneumothorax (OR, 1.005; 95% CI, 1.003-1.007), presence of ipsilateral atelectasis (OR, 3.508; 95% CI, 1.509-8.156) and a small amount of ipsilateral pleural effusion (OR, 5.277; 95% CI, 2.55-10.919) had significant effects on the increasing PPV. Therefore, PPV for pneumothorax diagnosis using AI can vary based on patients' factors, image-acquisition protocols, and the presence of concurrent lesions on CXR., (© 2024. The Author(s).)

Iatrogenic pneumothorax is a relevant complication of computed tomography (CT)-guided percutaneous lung biopsy. The aim of the present study was to analyze the prognostic significance of texture analysis, emphysema score and muscle mass derived from CT-imaging to predict postinterventional pneumothorax after CT-guided lung biopsy. Consecutive patients undergoing CT-guided percutaneous lung biopsy between 2012 and 2021 were analyzed. Multivariate logistic regression analysis included clinical risk factors and CT-imaging features to detect associations with pneumothorax development. Overall, 479 patients (178 females, mean age 65 ± 11.7 years) underwent CT-guided percutaneous lung biopsy of which 180 patients (37.5%) developed pneumothorax including 55 patients (11.5%) requiring chest tube placement. Risk factors associated with pneumothorax were chronic-obstructive pulmonary disease (COPD) (p = 0.03), age (p = 0.02), total lung capacity (p < 0.01) and residual volume (p = 0.01) as well as interventional parameters needle length inside the lung (p < 0.001), target lesion attached to pleura (p = 0.04), and intervention duration (p < 0.001). The combined model demonstrated a prediction accuracy of the occurrence of pneumothorax with an AUC of 0.78 [95%CI: 0.70-0.86] with a resulting sensitivity 0.80 and a specificity of 0.66. In conclusion, radiomics features of the target lesion and the lung lobe CT-emphysema score are predictive for the occurrence of pneumothorax and need for chest insertion after CT-guided lung biopsy., (© 2024. The Author(s).)

Background: Spontaneous hemopneumothorax is a rare condition that can be life-threatening if not promptly diagnosed and treated. We report a case of early treatment with transcatheter arterial embolization and video-assisted thoracoscopic surgery., Case Presentation: A 19-year-old Japanese male was diagnosed with left pneumothorax and underwent chest tube drainage. A total of 10 hours after admission, the patient developed dyspnea, chest pain, and sudden massive bloody effusion. Contrast-enhanced computed tomography revealed contrast extravasation near the left lung apex, and spontaneous hemopneumothorax was diagnosed. Angiography revealed bleeding from a branch of the subscapular artery and transcatheter arterial embolization was performed. The patient underwent video-assisted thoracoscopic surgery and recovered uneventfully., Conclusions: Anesthesiologists involved in urgent surgeries must be aware that a patient with spontaneous pneumothorax can develop a hemopneumothorax, even when full lung expansion has been obtained following chest tube drainage, owing to latent aberrant artery disruption. Interprofessional team engagement is essential for spontaneous hemopneumothorax management., (© 2024. The Author(s).)

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., Competing Interests: Financial/Nonfinancial Disclosures The authors have reported to CHEST the following: R. A. declares to have shares of WaveBase, Inc. None declared (H. C. F., D. S., D. W., C. D., J. T., B. W., B. V. B., R. P., R. M.)., (Copyright © 2024 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: Pneumothorax is a common acute presentation in healthcare settings. A chest radiograph (CXR) is often necessary to make the diagnosis, and minimizing the time between presentation and diagnosis is critical to deliver optimal treatment. Deep learning (DL) algorithms have been developed to rapidly identify pathologic findings on various imaging modalities., Purpose: The purpose of this systematic review and meta-analysis was to evaluate the overall performance of studies utilizing DL algorithms to detect pneumothorax on CXR., Methods: A study protocol was created and registered a priori (PROSPERO CRD42023391375). The search strategy included studies published up until January 10, 2023. Inclusion criteria were studies that used adult patients, utilized computer-aided detection of pneumothorax on CXR, dataset was evaluated by a qualified physician, and sufficient data was present to create a 2 × 2 contingency table. Risk of bias was assessed using the QUADAS-2 tool. Bivariate random effects meta-analyses and meta-regression modeling were performed., Results: Twenty-three studies were selected, including 34 011 patients and 34 075 CXRs. The pooled sensitivity and specificity were 87% (95% confidence interval, 81%, 92%) and 95% (95% confidence interval, 92%, 97%), respectively. The study design, use of an institutional/public data set and risk of bias had no significant effect on the sensitivity and specificity of pneumothorax detection., Conclusions: The relatively high sensitivity and specificity of pneumothorax detection by deep-learning showcases the vast potential for implementation in clinical settings to both augment the workflow of radiologists and assist in more rapid diagnoses and subsequent patient treatment., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Our aim was to review and update the existing evidence-based and consensus-based recommendations for the management of chest injuries in patients with multiple and/or severe injuries in the prehospital setting. This guideline topic is part of the 2022 update of the German Guideline on the Treatment of Patients with Multiple and/or Severe Injuries., Methods: MEDLINE and Embase were systematically searched to May 2021. Further literature reports were obtained from clinical experts. Randomised controlled trials, prospective cohort studies, cross-sectional studies, and comparative registry studies were included if they compared interventions for the detection and management of chest injuries in severely injured patients in the prehospital setting. We considered patient-relevant clinical outcomes such as mortality and diagnostic test accuracy. Risk of bias was assessed using NICE 2012 checklists. The evidence was synthesised narratively, and expert consensus was used to develop recommendations and determine their strength., Results: Two new studies were identified, both investigating the accuracy of in-flight ultrasound in the detection of pneumothorax. Two new recommendations were developed, one recommendation was modified. One of the two new recommendations and the modified recommendation address the use of ultrasound for detecting traumatic pneumothorax. One new good (clinical) practice point (GPP) recommends the use of an appropriate vented dressing in the management of open pneumothorax. Eleven recommendations were confirmed as unchanged because no new high-level evidence was found to support a change., Conclusion: Some evidence suggests that ultrasound should be considered to identify pneumothorax in the prehospital setting. Otherwise, the recommendations from 2016 remained unchanged., (© 2024. The Author(s).)

Ventilation-perfusion SPECT with or without CT using technetium 99m ( 99m Tc)-diethylenetriaminepentaacetic acid (DTPA) has been used to identify patterns typical of cardiopulmonary diseases, such as pulmonary embolism, pneumonia, heart failure, and obstructive lung disease. This case demonstrates the utility of a ventilation scan with SPECT/CT using 99m Tc-DTPA for investigating the cause of a persistent complex pneumothorax in a patient with severe chronic obstructive pulmonary disease who recently underwent endobronchial valve placement. Keywords: CT-Spectral Imaging (Multienergy), SPECT/CT, Thorax, Lung Supplemental material is available for this article. © RSNA, 2024.

Objective: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement., Method: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of the explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template's boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det)., Results: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach., Conclusions: In the context of pneumothorax diagnoses, we proposed a template-guided approach for improving model explanations. Our approach not only aligns model explanations more closely with clinical insights but also exhibits extensibility to other thoracic diseases. We anticipate that our template guidance will forge a novel approach to elucidating AI models by integrating clinical domain expertise., 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 Elsevier Inc. All rights reserved.)

Radiology narrative reports often describe characteristics of a patient's disease, including its location, size, and shape. Motivated by the recent success of multimodal learning, we hypothesized that this descriptive text could guide medical image analysis algorithms. We proposed a novel vision-language model, ConTEXTual Net, for the task of pneumothorax segmentation on chest radiographs. ConTEXTual Net extracts language features from physician-generated free-form radiology reports using a pre-trained language model. We then introduced cross-attention between the language features and the intermediate embeddings of an encoder-decoder convolutional neural network to enable language guidance for image analysis. ConTEXTual Net was trained on the CANDID-PTX dataset consisting of 3196 positive cases of pneumothorax with segmentation annotations from 6 different physicians as well as clinical radiology reports. Using cross-validation, ConTEXTual Net achieved a Dice score of 0.716±0.016, which was similar to the degree of inter-reader variability (0.712±0.044) computed on a subset of the data. It outperformed vision-only models (Swin UNETR: 0.670±0.015, ResNet50 U-Net: 0.677±0.015, GLoRIA: 0.686±0.014, and nnUNet 0.694±0.016) and a competing vision-language model (LAVT: 0.706±0.009). Ablation studies confirmed that it was the text information that led to the performance gains. Additionally, we show that certain augmentation methods degraded ConTEXTual Net's segmentation performance by breaking the image-text concordance. We also evaluated the effects of using different language models and activation functions in the cross-attention module, highlighting the efficacy of our chosen architectural design., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Background: While spontaneous pneumothorax has been documented in COVID-19 patients, reports on recurrent spontaneous pneumothorax due to cystic lesions in convalescent COVID-19 patients are scarce. The progression of these lung cystic lesions remains inadequately explored., Case Presentation and Literature Review: An 81-year-old male, a non-smoker with a history of rheumatoid arthritis, presented with fever, cough, and expectoration for 14 days. Initially diagnosed with moderate COVID-19, he deteriorated to severe COVID-19 despite adherence to local treatment guidelines. Successive identification of three cystic lesions termed "bulla" or "pneumatocele", and one cystic lesion with air-fluid level, referred to as "pneumo-hamatocele" (PHC), occurred in his lungs. Gradual improvement followed anti-inflammatory therapy and optimal supportive care. However, on day 42, sudden worsening dyspnea prompted a computed tomography (CT) scan, confirming a right spontaneous pneumothorax and subcutaneous emphysema, likely due to PHC rupture. Discharge followed chest tube implementation for pneumothorax resolution. On day 116, he returned to the hospital with mild exertional dyspnea. Chest CT revealed recurrent right pneumothorax from a remaining cyst in the right lung. Apart from our patient, literature retrieval identified 22 COVID-19 patients with spontaneous pneumothorax due to cystic lesions, with a male predominance (95.6%; 22/23). Diagnosis of pneumothorax and lung cystic lesions occurred around day 29.5 (range: 18-35) and day 26.4 (± 9.8) since symptom onset, respectively. Except for one patient whose pneumothorax occurred on day seven of illness, all patients eventually recovered., Conclusions: Recurrent spontaneous pneumothorax secondary to lung cystic lesions may manifest in convalescent COVID-19 patients, particularly males with COVID-19 pneumonia. Chest CT around 2 to 3 weeks post-symptom onset may be prudent to detect cystic lesion development and anticipate spontaneous pneumothorax., (© 2024. The Author(s).)

Objective. The trend in the medical field is towards intelligent detection-based medical diagnostic systems. However, these methods are often seen as 'black boxes' due to their lack of interpretability. This situation presents challenges in identifying reasons for misdiagnoses and improving accuracy, which leads to potential risks of misdiagnosis and delayed treatment. Therefore, how to enhance the interpretability of diagnostic models is crucial for improving patient outcomes and reducing treatment delays. So far, only limited researches exist on deep learning-based prediction of spontaneous pneumothorax, a pulmonary disease that affects lung ventilation and venous return. Approach. This study develops an integrated medical image analysis system using explainable deep learning model for image recognition and visualization to achieve an interpretable automatic diagnosis process. Main results. The system achieves an impressive 95.56% accuracy in pneumothorax classification, which emphasizes the significance of the blood vessel penetration defect in clinical judgment. Significance. This would lead to improve model trustworthiness, reduce uncertainty, and accurate diagnosis of various lung diseases, which results in better medical outcomes for patients and better utilization of medical resources. Future research can focus on implementing new deep learning models to detect and diagnose other lung diseases that can enhance the generalizability of this system., (Creative Commons Attribution license.)

Competing Interests: Disclosure The authors report no conflicts of interest in this work.

Purpose: The aim is to devise a machine learning algorithm exploiting preoperative clinical data to forecast the hazard of pneumothorax post-coaxial needle lung biopsy (CCNB), thereby informing clinical decision-making and enhancing perioperative care., Method: This retrospective analysis aggregated clinical and imaging data from patients with lung nodules (≤3 cm) biopsies. Variable selection was done using univariate analysis and LASSO regression, with the dataset subsequently divided into training (80 %) and validation (20 %) subsets. Various machine learning (ML) classifiers were employed in a consolidated approach to ascertain the paramount model, which was followed by individualized risk profiling showcased through Shapley Additive eXplanations (SHAP)., Results: Out of the 325 patients included in the study, 19.6% (64/325) experienced postoperative pneumothorax. High-risk factors determined were Cancer, Lesion&#95;type, GOLD, Size, and Depth. The Gaussian Naive Bayes (GNB) classifier demonstrated superior prediction with an Area Under the Curve (AUC) of 0.82 (95% CI 0.71-0.94), complemented by an accuracy rate of 0.8, sensitivity of 0.71, specificity of 0.84, and an F1 score of 0.61 in the test cohort., Conclusion: The formulated prognostic algorithm exhibited commendable efficacy in preoperatively prognosticating CCNB-induced pneumothorax, harboring the potential to refine personalized risk appraisals, steer clinical judgment, and ameliorate perioperative patient stewardship., 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 B.V.)

Background: Traumatic pneumothorax (PTX) is a common occurrence in thoracic trauma patients, with a majority requiring tube thoracostomy (TT) for management. Recently, the "35-mm" rule has advocated for observation of patients with PTX less than 35 mm on chest computed tomography (CT) scan. This rule has not been examined in chest x-ray (CXR). We hypothesize that a similar size cutoff can be determined in CXR predictive of need for tube thoracostomy., Methods: We performed a single-institution retrospective review of patients with traumatic PTX from 2018 to 2022, excluding those who underwent TT prior to CXR. Primary outcomes were size of pneumothorax on CXR and need for TT; secondary outcome was failed observation, defined as TT more than 4 hours after presentation. To determine the size cutoff on CXR to predict TT need, area under the receiver operating curve (AUROC) analyses were performed and Youden's index calculated (significance at p < 0.05). Predictors of failure were calculated using logistic regression., Results: There were 341 pneumothoraces in 304 patients (94.4% blunt trauma, median injury severity score 14). Of these, 82 (24.0%) had a TT placed within the first 4 hours. Fifty-five of observed patients (21.2%) failed, and these patients had a larger PTX on CXR (8.6 mm [5.0-18.0 mm] vs. 0.0 mm [0.0-2.3 mm] ( p < 0.001)). Chest x-ray PTX size correlated moderately with CT size (r = 0.31, p < 0.001) and was highly predictive of need for TT insertion (AUC 0.75, p < 0.0001), with an optimal size cutoff predicting TT need of 38 mm., Conclusion: Chest x-ray imaging size was predictive of need for TT, with an optimal size cutoff on CXR of 38 mm, approaching the "35-mm rule." In addition to size, failed observation was predicted by presenting lactic acidosis and need for supplemental oxygen. This demonstrates this cutoff should be considered for prospective study in CXR., Level of Evidence: Therapeutic/Care Management; Level IV., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Screening for pneumothorax (PTX) is standard practice after thoracostomy tube removal, with postpull CXR being the gold standard. However, studies have shown that point-of-care thoracic ultrasound (POCTUS) is effective at detecting PTX and may represent a viable alternative. This study aims to evaluate the safety and efficacy of POCTUS for evaluation of clinically significant postpull PTX compared with chest x-ray (CXR)., Methods: We performed a prospective, cohort study at a Level 1 trauma center between April and December 2022 comparing the ability of POCTUS to detect clinically significant postpull PTX compared with CXR. Patients with thoracostomy tube placed for PTX, hemothorax, or hemopneumothorax were included. Clinically insignificant PTX was defined as a small residual or apical PTX without associated respiratory symptoms or need for thoracostomy tube replacement while clinically significant PTX were moderate to large or associated with physiologic change., Results: We included 82 patients, the most common etiology was blunt trauma (n = 57), and the indications for thoracostomy tube placement were: PTX (n = 38), hemothorax (n = 15), and hemopneumothorax (n = 14). One patient required thoracostomy tube replacement for recurrent PTX identified by both ultrasound and X-ray. Thoracic ultrasound had a sensitivity of 100%, specificity of 95%, positive predictive value of 60%, and negative predictive value of 100% for the detection of clinically significant postpull PTX., Conclusions: The use of POCTUS for the detection of clinically significant PTX after thoracostomy tube removal is a safe and effective alternative to standard CXR. This echoes similar studies and emphasizes the need for further investigation in a multicenter study., (Published by Elsevier Inc.)

Implantation of cardiac devices is usually considered to be a safe procedure. Rare complications, such as pneumothorax, may occur after the procedure. The association with pneumopericardium or pneumomediastinum is even more uncommon. We present the case of a patient in his 70s, on haemodialysis, admitted for complete atrioventricular block. He underwent implantation of a dual-chamber pacemaker. He presented with chest pain the day after implantation. Chest CT scan revealed a pneumothorax associated with a pneumopericardium and pneumomediastinum 'pan pneumo', due to an atrial perforation. We opted for a conservative management strategy. Repeat CT scan of the chest 8 days after the procedure showed a complete resorption of the 'pan pneumo'. The objective of this case report is to describe this rare complication and provide further insight into its management, particularly in the absence of specific guidelines., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

In this case report, a 19-year-old woman suffered a high-energy trauma to her left side with multiple left-sided fractures, including ribs 8-12, with no pneumothorax on the left side, but a large right-sided pneumothorax. Contralateral pneumothorax is extremely rare in primarily unilateral trauma, especially when no contralateral rib fractures are present. A possible explanation is that of a barotrauma due to increased intrathoracic pressure with external compression. Careful examination of both sides of the body is necessary when evaluating patients with unilateral trauma, as serious organ injury may occur contralaterally., (Published under Open Access CC-BY-NC-BD 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/.)

Objective: The mini-open lateral retropleural (MO-LRP) approach is an effective option for surgically treating thoracic disc herniations, but the approach raises concerns for pneumothorax (PTX). However, chest tube placement causes insertion site tenderness, necessitates consultation services, increases radiation exposure (requires multiple radiographs), delays the progression of care, and increases narcotic requirements. This study examined the incidence of radiographic and clinically significant PTX and hemothorax (HTX) after the MO-LRP approach, without the placement of a prophylactic chest tube, for thoracic disc herniation., Methods: This study was a single-institution retrospective evaluation of consecutive cases from 2017 to 2022. Electronic medical records were reviewed, including postoperative chest radiographs, radiology and operative reports, and postoperative notes. The presence of PTX or HTX was determined on chest radiographs obtained in all patients immediately after surgery, with interval radiographs if either was present. The size was categorized as large (≥ 3 cm) or small (< 3 cm) based on guidelines of the American College of Chest Physicians. PTX or HTX was considered clinically significant if it required intervention., Results: Thirty patients underwent thoracic discectomy via the MO-LRP approach. All patients were included. Twenty patients were men (67%), and 10 (33%) were women. The patients ranged in age from 25 to 74 years. The most commonly treated level was T11-12 (n = 11, 37%). Intraoperative violation of parietal pleura occurred in 5 patients (17%). No patient had prophylactic chest tube placement. Fifteen patients (50%) had PTX on postoperative chest radiographs; 2 patients had large PTXs, and 13 had small PTXs. Both patients with large PTXs had expansion on repeat radiographs and were treated with chest tube insertion. Of the 13 patients with a small PTX, 1 required 100% oxygen using a nonrebreather mask; the remainder were asymptomatic. One patient, who had no abnormal findings on the immediate postoperative chest radiograph, developed an incidental HTX on postoperative day 6 and was treated with chest tube insertion. Thus, 3 patients (10%) required a chest tube: 2 for expanding PTX and 1 for delayed HTX., Conclusions: Most patients who undergo thoracic discectomy via the MO-LRP approach do not develop clinically significant PTX or HTX. PTX and HTX in this patient population should be treated with a chest tube only when there are postoperative clinical and radiographic indications.

Competing Interests: Competing interests: None declared.

Background: To investigate the risk factors of pneumothorax of using computed tomography (CT) guidance to inject autologous blood to locate isolated lung nodules., Methods: In the First Hospital of Putian City, 92 cases of single small pulmonary nodules were retrospectively analyzed between November 2019 and March 2023. Before each surgery, autologous blood was injected, and the complications of each case, such as pneumothorax and pulmonary hemorrhage, were recorded. Patient sex, age, position at positioning, and nodule type, size, location, and distance from the visceral pleura were considered. Similarly, the thickness of the chest wall, the depth and duration of the needle-lung contact, the length of the positioning procedure, and complications connected to the patient's positioning were noted. Logistics single-factor and multi-factor variable analyses were used to identify the risk factors for pneumothorax. The multi-factor logistics analysis was incorporated into the final nomogram prediction model for modeling, and a nomogram was established., Results: Logistics analysis suggested that the nodule size and the contact depth between the needle and lung tissue were independent risk factors for pneumothorax., Conclusion: The factors associated with pneumothorax after localization are smaller nodules and deeper contact between the needle and lung tissue., (© 2024. The Author(s).)

Introduction: Identify the risk factors for delayed pneumothorax after lung biopsy., Methods: A retrospective study of 355 CT-guided lung biopsies was performed at Fiona Stanley Hospital, Western Australia over 42 months. A comprehensive range of patient, lesion and procedural variables were recorded. All post-procedural complications including time, size of pneumothorax and post-biopsy radiographs were reviewed. Lasso logistic regression model was utilised to determine factors predicting patient complications., Results: A total of 167 patients (47%) developed a pneumothorax, in which 34% were significant, requiring longer observation or drain insertion. The majority of pneumothoraces occurred within the first hour (86%), with 90% detected at the time of the procedure. Then, 12% were detected more than 3 h post-procedure, of which 8 patients (5%) had a significant delayed pneumothorax. Factors increasing the likelihood of significant pneumothorax include the length of lung traversed, smaller nodule size, surrounding emphysema, increased age and lateral patient position with the lesion in the non-dependent aspect. Increasing patient age, longer length of lung traversed and smaller nodule diameter increase the risk of delayed onset of pneumothorax (more than 3 h)., Conclusion: The results of this study align with other studies indicating it is safe to discharge stable patients within an hour post-lung biopsy. However, specific risk factors, including age, small lesion size and deep lesions, may identify patients who could benefit from a longer observation period., (© 2024 Royal Australian and New Zealand College of Radiologists.)

Purpose: The primary goal of this study was to determine the anatomic relationship between the clavicle and the apical lung segment. The secondary goal was to determine the incidence of pneumothorax (PTX) in patients who underwent clavicle ORIF to analyze the utility of postoperative chest radiographs., Methods: Six hundred thirty-one patients with a midshaft clavicle fracture who underwent superior plating at a single institution were identified. Forty-two patients had a CT scan of the chest. Three points on the uninjured clavicle were defined: 2 cm from the medial end of the clavicle, the mid-point of the clavicle, and 2 cm from the lateral end of the clavicle. At each point, the distance from both the inferior cortex and the superior cortex of the clavicle to the apical lung segment was measured. All 631 patients who underwent Open Reduction and Internal Fixation had a postoperative chest radiograph to evaluate implant placement, restoration of clavicular length, and presence of PTX., Results: From the lateral end of the clavicle, the mean distance of the lung was 60.0 ± 14.9 mm (20.1 to 96.1 mm) from the inferior cortex of the clavicle. At the mid-point, the mean distance of the lung was 32.3 ± 7.2 mm (20.4 to 45.5 mm) from the inferior cortex of the clavicle. At the medial end, the mean distance of the lung was 18.0 ± 5.5 mm (8.1 to 28.9 mm) from the inferior cortex of the clavicle. A review of postoperative radiographs for all 631 patients revealed none (0%) with a postoperative iatrogenic PTX., Conclusion: The risk of injury is minimal in all three zones. Postoperative chest radiographs after clavicle fracture repair to rule out PTX are unnecessary., (Copyright © 2024 by the American Academy of Orthopaedic Surgeons.)

A 24-year-old male patient, without further symptoms or comorbidities presented to the emergency room with acute dyspnea after heavy lifting two days before. On auscultation an attenuated vesicular breath was noticed on the right lung. In the initial chest radiograph a right-sided primary spontaneous pneumothorax with minor mediastinal shift was diagnosed. After insertion of a 12-French chest tube the patient's clinical condition deteriorated. The following chest radiograph and computed tomography of the thorax showed a reexpansion pulmonary edema in the right lung. The patient was admitted to the ICU and supportive treatment was initiated. Pulmonary reexpansion edema after drainage of a pneumothorax is a very rare complication with mortality rates reaching up to 20%. The exact pathophysiology remains unknown. Typical Symptoms include dyspnea, hypotension, and tachycardia. To minimize the risk of a pulmonary reexpansion edema, not more than 1200-1800 ml of air should be drained at once and the drainage should be stopped when the patient starts coughing., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Purpose To develop a prediction model combining both clinical and CT texture analysis radiomics features for predicting pneumothorax complications in patients undergoing CT-guided core needle biopsy. Materials and Methods A total of 424 patients (mean age, 65.6 years ± 12.7 [SD]; 232 male, 192 female) who underwent CT-guided core needle biopsy between January 2021 and October 2022 were retrospectively included as the training data set. Clinical and procedure-related characteristics were documented. Texture analysis radiomics features were extracted from the subpleural lung parenchyma traversed by needle. Moderate pneumothorax was defined as a postprocedure air rim of 2 cm or greater. The prediction model was developed using logistic regression with backward elimination, presented by linear fusion of the selected features weighted by their coefficients. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Validation was conducted in an external cohort ( n = 45; mean age, 58.2 years ± 12.7; 19 male, 26 female) from a different hospital. Results Moderate pneumothorax occurred in 12.0% (51 of 424) of the training cohort and 8.9% (four of 45) of the external test cohort. Patients with emphysema ( P < .001) or a longer needle path length ( P = .01) exhibited a higher incidence of moderate pneumothorax in the training cohort. Texture analysis features, including gray-level co-occurrence matrix cluster shade ( P < .001), gray-level run-length matrix low gray-level run emphasis ( P = .049), gray-level run-length matrix run entropy ( P = .003), gray-level size-zone matrix gray-level variance ( P < .001), and neighboring gray-tone difference matrix complexity ( P < .001), showed higher values in patients with moderate pneumothorax. The combined clinical-radiomics model demonstrated satisfactory performance in both the training (AUC 0.78, accuracy = 71.9%) and external test cohorts (AUC 0.86, accuracy 73.3%). Conclusion The model integrating both clinical and radiomics features offered practical diagnostic performance and accuracy for predicting moderate pneumothorax in patients undergoing CT-guided core needle biopsy. Keywords: Biopsy/Needle Aspiration, Thorax, CT, Pneumothorax, Core Needle Biopsy, Texture Analysis, Radiomics, CT Supplemental material is available for this article . © RSNA, 2024.

Purpose: To study the performance of artificial intelligence (AI) for detecting pleural pathology on chest radiographs (CXRs) using computed tomography as ground truth., Patients and Methods: Retrospective study of subjects undergoing CXR in various clinical settings. Computed tomography obtained within 24 hours of the CXR was used to volumetrically quantify pleural effusions (PEfs) and pneumothoraxes (Ptxs). CXR was evaluated by AI software (INSIGHT CXR; Lunit) and by 3 second-year radiology residents, followed by AI-assisted reassessment after a 3-month washout period. We used the area under the receiver operating characteristics curve (AUROC) to assess AI versus residents' performance and mixed-model analyses to investigate differences in reading time and interreader concordance., Results: There were 96 control subjects, 165 with PEf, and 101 with Ptx. AI-AUROC was noninferior to aggregate resident-AUROC for PEf (0.82 vs 0.86, P < 0.001) and Ptx (0.80 vs 0.84, P = 0.001) detection. AI-assisted resident-AUROC was higher but not significantly different from the baseline. AI-assisted reading time was reduced by 49% (157 vs 80 s per case, P = 0.009), and Fleiss kappa for Ptx detection increased from 0.70 to 0.78 ( P = 0.003). AI decreased detection error for PEf (odds ratio = 0.74, P = 0.024) and Ptx (odds ratio = 0.39, P < 0.001)., Conclusion: Current AI technology for the detection of PEf and Ptx on CXR was noninferior to second-year resident performance and could help decrease reading time and detection error., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Pneumothorax following shoulder arthroscopy, although rare, is documented in over 30 PubMed case reports as occurring during or within 10 hours post-procedure., Case Presentation: A fit septuagenarian underwent a two-hour arthroscopic rotator cuff repair with IV anaesthesia and laryngeal mask airway, without a nerve block. With one hour remaining of the operation, the patient had desaturation and hypotension. Lung sliding was absent on ultrasound and x-ray confirmed left-sided tension pneumothorax. Successful thoracic drain insertion and lung re-expansion facilitated his recovery, allowing discharge after 24 hours and symptom-free status at 6 months., Interpretation: This case highlights pneumothorax as an uncommon yet possible post-arthroscopic event. The speculated aetiology is the surgical procedure, where pump-induced pressure fluctuations may displace air into surrounding tissue. Instances of pneumomediastinum and subcutaneous emphysema without pneumothorax suggest arthroscopic origin of air. Prompt perioperative ultrasound can aid in detecting such critical complications.