Managing acute respiratory distress syndrome (ARDS) invariably involves the administration of mechanical ventilation, the challenge being to avoid the iatrogenic sequellum known as ventilator-induced lung injury (VILI). Devising individualized ventilation strategies in ARDS requires that patient-specific lung physiology be taken into account, and this is greatly aided by the use of computational models of lung mechanical function that can be matched to physiological measurements made in a given patient. In this review, we discuss recent models that have the potential to serve as the basis for devising minimally injurious modes of mechanical ventilation in ARDS patients.
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http://dx.doi.org/10.1016/j.ddmod.2014.02.005 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Med Phys
January 2025
Department of Engineering Physics, Tsinghua University, Beijing, China.
Background: X-ray grating-based dark-field imaging can sense the small angle scattering caused by object's micro-structures. This technique is sensitive to the porous microstructure of lung alveoli and has the potential to detect lung diseases at an early stage. Up to now, a human-scale dark-field CT (DF-CT) prototype has been built for lung imaging.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Orthopedic Surgery, Arrowhead Regional Medical Center, Colton, CA, USA.
Rib pathology is uniquely difficult and time-consuming for radiologists to diagnose. AI can reduce radiologist workload and serve as a tool to improve accurate diagnosis. To date, no reviews have been performed synthesizing identification of rib fracture data on AI and its diagnostic performance on X-ray and CT scans of rib fractures and its comparison to physicians.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
View Article and Find Full Text PDFBehav Res Methods
January 2025
CogNosco Lab, Department of Psychology and Cognitive Sciences, University of Trento, Trento, Italy.
We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically validated lexicons for detecting the eight emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or large language models like ChatGPT 3.
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