Ultrasonography and elastography are the most widely used imaging modalities for diagnosing non-alcoholic fatty liver disease. This study aimed to assess and compare the diagnostic accuracy in patients with non-alcoholic fatty liver disease/non-alcoholic steatohepatitis. This systematic review was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic search was done for the past seven years using Pubmed, Pubmed Central, Cochrane, and Google Scholar databases on Jun 29, 2022. Studies were included based on the following predefined criteria: observational studies, randomized controlled trial (RCT), comparative studies, studies using liver biopsy or MRI proton density fat fraction (MRI PDFF) as a reference standard, ultrasonography, and elastography with measures of their diagnostic accuracy like sensitivity (SN), specificity (SP), area under the receiver operating characteristic (AUROC) curve, and English language. The data were extracted on a predefined template. The final twelve eligible studies were assessed using the quality assessment of diagnostic accuracy tool (QUADS-2). Most studies focused on elastography techniques, and the remaining focused on quantitative ultrasonography methods like the controlled attenuation parameter (CAP) and attenuation coefficient (AC). Only one study was available for the evaluation of qualitative ultrasonography. MRI was generally found superior to other diagnostic tests for determining liver stiffness through magnetic resonance elastography (MRE) and steatosis through MRI PDFF. Data assessing the comparative diagnostic accuracy of the two tests were inconclusive.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637432 | PMC |
http://dx.doi.org/10.7759/cureus.29967 | DOI Listing |
Nanomedicine (Lond)
January 2025
Department of Ultrasound, Yantaishan Hospital, Binzhou Medical University, Yantai, Shandong, China.
With the rapid development of nanotechnology, nanoultrasonography has emerged as a promising medical imaging technique that demonstrates significant potential in the diagnosis and treatment of gastrointestinal (GI) diseases. This review discusses the applications of nanoultrasonography in the gastrointestinal field, including improvements in imaging resolution, diagnostic accuracy, latest research findings, and prospects for clinical application. By analyzing existing literature, we explore the role of nanoultrasonography in enhancing imaging resolution, enabling targeted drug delivery, and improving therapeutic outcomes, thereby providing a reference for future research directions.
View Article and Find Full Text PDFClin Implant Dent Relat Res
February 2025
SEMRUK Technology Inc., Cumhuriyet Teknokent, Sivas, Turkiye.
Objectives: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.
Materials And Methods: A retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized.
Eur Heart J Digit Health
January 2025
Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, SE-182 88 Stockholm, Sweden.
Aims: A simplified version of the history, electrocardiogram, age, risk factors, troponin (HEART) score, excluding troponin, has been proposed to rule-out major adverse cardiac events (MACEs). Computerized history taking (CHT) provides a systematic and automated method to obtain information necessary to calculate the HEAR score. We aimed to evaluate the efficacy and diagnostic accuracy of CHT in calculating the HEAR score for predicting MACE.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Cardiovascular Center, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA.
Aims: This study evaluates the performance of OpenAI's latest large language model (LLM), Chat Generative Pre-trained Transformer-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP).
Methods And Results: Chat Generative Pre-trained Transformer-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based [electrocardiogram (ECG), angiogram, computed tomography (CT) scan, and echocardiogram] formats.
Eur Heart J Digit Health
January 2025
Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, No. 88 West Taishan Road, Zhuzhou 412007, Hunan, China.
Aims: The electrocardiogram (ECG) is the primary method for diagnosing atrial fibrillation (AF), but interpreting ECGs can be time-consuming and labour-intensive, which deserves more exploration.
Methods And Results: We collected ECG data from 6590 patients as YY2023, classified as Normal, AF, and Other. Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), and Attention construct the AF recognition model CNN BiLSTM Attention-Atrial Fibrillation (CLA-AF).
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!