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PBCS-ConvNeXt: Convolutional Network-Based Automatic Diagnosis of Non-alcoholic Fatty Liver in Abdominal Ultrasound Images.
J Imaging Inform Med
January 2025
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent chronic liver condition characterized by excessive hepatic fat accumulation. Early diagnosis is crucial as NAFLD can progress to more severe conditions like steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma without timely intervention. While liver biopsy remains the gold standard for NAFLD assessment, abdominal ultrasound (US) imaging has emerged as a widely adopted non-invasive modality due to convenience and low cost.
View Article and Find Full Text PDFMolecular cytogenetic study of the fetal genome in idiopathic recurrent pregnancy loss.
Int J Gynaecol Obstet
January 2025
Scientific Centre of Obstetrics, Gynecology and Perinatology, Almaty, Republic of Kazakhstan.
Objective: Despite numerous studies on the causes of recurrent pregnancy loss (RPL), nearly half of cases remain unidentified, which determines the research relevance. This study aims to investigate microchromosomal variations in the fetal genome associated with the development of idiopathic RPL.
Methods: The research was supported by the Centre for Molecular Medicine and the Research Institute of Obstetrics, Gynecology and Perinatology and conducted over a period of 2 years.
Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets.
Front Artif Intell
January 2025
Department of Computer and Automatic Control, Faculty of Engineering, Tanta University, Tanta, Egypt.
Introduction: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.
Methods: A novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed.
Impact of A1 segment asymmetry on hemodynamic conditions around the circle of Willis and anterior communicating artery aneurysm formation.
Front Neurol
January 2025
Department of Neurology, Ajou University School of Medicine, Suwon, Republic of Korea.
Background: This study aims to investigate how A1 segment asymmetry-also known as A1 dominancy-influences the development of the anterior communicating artery aneurysm (AcomA) as it affects hemodynamic conditions within the circle of Willis (COW). Using time-of-flight magnetic resonance angiography (TOF-MRA), the research introduces a novel approach to assessing shear stress in A1 segments to uncover the hemodynamic factors contributing to AcomA formation.
Method: An observational study was conducted over 6 years at a tertiary university hospital's outpatient clinic.
Research on a traditional Chinese medicine case-based question-answering system integrating large language models and knowledge graphs.
Front Med (Lausanne)
January 2025
School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China.
Introduction: Traditional Chinese Medicine (TCM) case records encapsulate vast clinical experiences and theoretical insights, holding significant research and practical value. However, traditional case studies face challenges such as large data volumes, complex information, and difficulties in efficient retrieval and analysis. This study aimed to address these issues by leveraging modern data techniques to improve access and analysis of TCM case records.
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