Introduction: Our objective was to measure the diagnostic accuracy of a novel software technology to detect pneumothorax on Brightness (B) mode and Motion (M) mode ultrasonography.
Methods: Ultrasonography fellowship-trained emergency physicians performed thoracic ultrasonography at baseline and after surgically creating a pneumothorax in eight intubated, spontaneously breathing porcine subjects. Prior to pneumothorax induction, we captured sagittal M-mode still images and B-mode videos of each intercostal space with a linear array transducer at 4cm of depth. After collection of baseline images, we placed a chest tube, injected air into the pleural space in 250mL increments, and repeated the ultrasonography for pneumothorax volumes of 250mL, 500mL, 750mL, and 1000mL. We confirmed pneumothorax with intrapleural digital manometry and ultrasound by expert sonographers. We exported collected images for interpretation by the software. We treated each individual scan as a single test for interpretation by the software.
Results: Excluding indeterminate results, we collected 338M-mode images for which the software demonstrated a sensitivity of 98% (95% confidence interval [CI] 92-99%), specificity of 95% (95% CI 86-99), positive likelihood ratio (LR+) of 21.6 (95% CI 7.1-65), and negative likelihood ratio (LR-) of 0.02 (95% CI 0.008-0.046). Among 364 B-mode videos, the software demonstrated a sensitivity of 86% (95% CI 81-90%), specificity of 85% (81-91%), LR+ of 5.7 (95% CI 3.2-10.2), and LR- of 0.17 (95% CI 0.12-0.22).
Conclusions: This novel technology has potential as a useful adjunct to diagnose pneumothorax on thoracic ultrasonography.
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http://dx.doi.org/10.1016/j.ajem.2017.03.073 | DOI Listing |
Sci Rep
December 2024
School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Life Sciences Building 85, University Road, Highfield, Southampton, SO17 1BJ, UK.
Osteoarthritis (OA) is a complex disease of cartilage characterised by joint pain, functional limitation, and reduced quality of life with affected joint movement leading to pain and limited mobility. Current methods to diagnose OA are predominantly limited to X-ray, MRI and invasive joint fluid analysis, all of which lack chemical or molecular specificity and are limited to detection of the disease at later stages. A rapid minimally invasive and non-destructive approach to disease diagnosis is a critical unmet need.
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December 2024
Longyan First Affiliated Hospital of Fujian Medical University, Longyan, 364000, Fujian, China.
The monocyte-to-Apolipoprotein A1 ratio (MAR) emerges as a potentially valuable inflammatory biomarker indicative of metabolic dysfunction-associated fatty liver disease (MASLD). Accordingly, this investigation primarily aims to assess the correlation between MAR and MASLD risk. A cohort comprising 957 individuals diagnosed with type 2 diabetes mellitus (T2DM) participated in this study.
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December 2024
Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, Republic of Korea.
Polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of backscattered light from tissues and provides valuable insights into the birefringence properties of biological tissues. Contrastive unpaired translation (CUT) was used in this study to generate a synthetic PS-OCT image from a single OCT image. The challenges related to extensive data requirements relying on labeled datasets using only pixel-wise correlations that make it difficult to efficiently regenerate the periodic patterns observed in PS-OCT images were addressed.
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December 2024
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
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December 2024
Department of General Surgery, Cancer center, Division of Hepatobiliary and Pancreatic Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, 310014, Hangzhou, Zhejiang Province, China.
Despite the growing adoption of laparoscopic hepatectomy (LH) for intrahepatic cholangiocarcinoma (ICC), there is no scoring system available designed to evaluate its surgical complexity. This paper aims to introduce a novel difficulty scoring system (DSS), designated as the Wei-DSS, exclusively tailored to assess the surgical difficulty of pure LH for ICC. We retrospectively collected clinical data from ICC patients who underwent pure LH at our institution, spanning from November 2018 to May 2024.
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