The Southampton electrical impedance tomography (EIT) system used a Sheffield data acquisition unit and a PC based 'Harlequin' transputer card to reconstruct and display images of the distribution of internal conductivity within the thorax. The system produces real-time images relating to both cardiac and pulmonary function. As a first step towards diagnosis using these images neural nets have been applied to the identification of regions of interest in the EIT images for which some activity with time, such as ventricular ejection, is sought. This paper addresses the use of a back-projection network to identify characteristic regions within the images. The network facilitates the production of automated real-time activity plots by defining their effective extent in the images of specific organs. The application is novel within the medical imaging field as the aim is to use neural networks for real-time image analysis.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1088/0143-0815/13/a/023 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Identification of Intracranial Germ Cell Tumors Based on Facial Photos: Exploratory Study on the Use of Deep Learning for Software Development.
J Med Internet Res
January 2025
Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background: Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.
View Article and Find Full Text PDFRisk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study.
J Med Internet Res
January 2025
Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
A comparative analysis of CNNs and LSTMs for ECG-based diagnosis of arrythmia and congestive heart failure.
Comput Methods Biomech Biomed Engin
January 2025
Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
Cardiac arrhythmias are major global health concern and their early detection is critical for diagnosis. This study comprehensively evaluates the effectiveness of CNNs and LSTMs for the classification of cardiac arrhythmias, considering three PhysioNet datasets. ECG records are segmented to accommodate around ∼10s of ECG data.
View Article and Find Full Text PDFASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.
PLoS Comput Biol
January 2025
School of Software, Taiyuan University of Technology, Taiyuan, China.
Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to these challenges, this study introduces the Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach to unraveling latent interactions in the complex context of cancer cell lines and drugs.
View Article and Find Full Text PDFSynchronization in spiking neural networks with short and long connections and time delays.
Chaos
January 2025
Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille 13005, France.
Article Synopsis
- Synchronization in brain networks is crucial for processing information, but time delays in signal transmission can significantly influence this process, especially in more complex spiking neural networks.
- The study involves investigating synchronization conditions and dynamics in a two-dimensional network of adaptive exponential integrate-and-fire neurons, focusing on how delay impacts this behavior.
- Findings reveal that synchronization patterns depend on a combination of properties at different levels, including individual neuron characteristics, network connectivity, and long-range connections, which together affect the emergent activity patterns in the brain.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!