Background: Over the past two decades, studies have demonstrated that lung ultrasound is useful in diagnosing alveolar interstitial syndrome, which is seen in patients with decompensated congestive heart failure (CHF).
Methods: We studied medical students performing lung ultrasound on patients admitted to the hospital with a presumed diagnosis of decompensated CHF in a prospective convenience observation study. Two ultrasound fellowship-trained emergency medicine attendings independently reviewed the lung ultrasounds at a later date, blinded to the students' interpretation and other clinical information, to confirm ultrasound findings and assess for inter-rater reliability of the lung ultrasound using intraclass correlation coefficients (ICCs).
Results: Thirty-six patients were enrolled in the study resulting in 653 unique lung zones scanned. The zones were imaged and classified as being normal (B-lines < 3) or pathologic (B-lines ≥ 3). The novice scanners' interpretation was compared to expert reviews using ICCs. The ICC was 0.88, with a 95% confidence interval of 0.87 to 0.90, for all lung zones scanned.
Conclusion: There was almost perfect agreement between novice practitioners and experts when determining the presence of pathologic B-lines in individual patients.
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http://dx.doi.org/10.1002/aet2.10584 | DOI Listing |
Int J Surg
January 2025
Carcinoma Department of Traditional Chinese Medicine, Dianjiang People's Hospital of Chongqing, Chongqing, PR China.
The widespread adoption of high-resolution computed tomography (CT) screening has led to increased detection of small pulmonary nodules, necessitating accurate localization techniques for surgical resection. This review examines the evolution, efficacy, and safety of various localization methods for small pulmonary nodules. Studies focusing on localization techniques for pulmonary nodules ≤30 mm in diameter were included, with emphasis on technical success rates and complication profiles.
View Article and Find Full Text PDFJ Intern Med
January 2025
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.
Background: Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.
Purpose: To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST).
Material And Methods: We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.
J Vis Exp
January 2025
Fever Outpatient Clinic, Dongzhimen Hospital, Affiliated to Beijing University of Chinese Medicine;
Non-invasive assessment of pulmonary nodule malignancy remains a critical challenge in lung cancer diagnosis. Traditional methods often lack precision in differentiating benign from malignant nodules, particularly in the early stages. This study introduces an approach using multifractal spectrum analysis to quantitatively evaluate pulmonary nodule characteristics.
View Article and Find Full Text PDFArtif Organs
January 2025
Department of Cardiovascular Surgery, Faculty of Medicine, University Medical Centre Freiburg, University of Freiburg, Freiburg, Germany.
Introduction: Veno-venous extracorporeal membrane oxygenation (V-V ECMO) is increasingly used in the treatment of severe respiratory failure. Despite a significant increase in the worldwide use of extracorporeal lung assist devices recirculation remains a common complication and is associated with a reduced effectiveness of ECMO support and increased hemolysis. In this observational study we aimed to investigate the impact of cannula configuration and extracorporeal flow on recirculation.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology, Peking University Third Hospital, Beijing, China.
Background: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.
Purpose: To develop and validate artificial intelligence (AI) models using noncontrast MRI to identify primary sites of spinal metastases, aiming to enhance diagnostic efficiency.
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