AIMC Topic: Liver
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A machine learning model for predicting abnormal liver function induced by a Chinese herbal medicine preparation (Zhengqing Fengtongning) in patients with rheumatoid arthritis based on real-world study.
Journal of integrative medicine
Dec 6, 2024
OBJECTIVE: Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients' quality of life. Zhengqing Fengtongning (ZF) is a traditional Chinese medicine preparation used to treat ...
Challenges and solutions of deep learning-based automated liver segmentation: A systematic review.
Computers in biology and medicine
Dec 5, 2024
The liver is one of the vital organs in the body. Precise liver segmentation in medical images is essential for liver disease treatment. The deep learning-based liver segmentation process faces several challenges. This research aims to analyze the ch...
Identification of structural stability and fragility of mouse liver glycogen via label-free Raman spectroscopy coupled with convolutional neural network algorithm.
International journal of biological macromolecules
Dec 3, 2024
Glycogen structure is closely associated with its physiological functions. Previous studies confirmed that liver glycogen structure had two dominant states: mainly stable during the day and largely fragile at night. However, the diurnal change of gly...
Liver tumor segmentation method combining multi-axis attention and conditional generative adversarial networks.
PloS one
Dec 3, 2024
In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However, this approach suffers from low efficiency and poor accuracy. With the development of deep learning,...
Comparison of deep learning schemes in grading non-alcoholic fatty liver disease using B-mode ultrasound hepatorenal window images with liver biopsy as the gold standard.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Dec 2, 2024
BACKGROUND/INTRODUCTION: To evaluate the performance of pre-trained deep learning schemes (DLS) in hepatic steatosis (HS) grading of Non-Alcoholic Fatty Liver Disease (NAFLD) patients, using as input B-mode US images containing right kidney (RK) cort...
Machine learning based on alcohol drinking-gut microbiota-liver axis in predicting the occurrence of early-stage hepatocellular carcinoma.
BMC cancer
Nov 29, 2024
BACKGROUND: Alcohol drinking and gut microbiota are related to hepatocellular carcinoma (HCC), but the specific relationship between them remains unclear.
Pediatric Liver Transplant Pathology: An Update and Practical Consideration.
Surgical pathology clinics
Nov 28, 2024
This review provides a summary of the diagnostic approach to pediatric liver transplantation (LT) pathology. It emphasizes the pathologic features of T-cell-mediated rejection, the most common finding on liver allograft biopsies, and discusses other ...
Convolutional neural network classification of ultrasound parametric images based on echo-envelope statistics for the quantitative diagnosis of liver steatosis.
Journal of medical ultrasonics (2001)
Nov 23, 2024
PURPOSE: Early detection and quantitative evaluation of liver steatosis are crucial. Therefore, this study investigated a method for classifying ultrasound images to fatty liver grades based on echo-envelope statistics (ES) and convolutional neural n...
Generalizability of lesion detection and segmentation when ScaleNAS is trained on a large multi-organ dataset and validated in the liver.
Medical physics
Nov 22, 2024
BACKGROUND: Tumor assessment through imaging is crucial for diagnosing and treating cancer. Lesions in the liver, a common site for metastatic disease, are particularly challenging to accurately detect and segment. This labor-intensive task is subjec...
Accuracy of deep learning-based attenuation correction in Tc-GSA SPECT/CT hepatic imaging.
Radiography (London, England : 1995)
Nov 15, 2024
INTRODUCTION: Attenuation correction (AC) is necessary for accurate assessment of radioactive distribution in single photon emission computed tomography (SPECT). The method of computed tomography-based AC (CTAC) is widely used because of its accuracy...
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