Artificial intelligence in imaging for liver disease diagnosis DOI Creative Commons
Chenglong Yin, Huafeng Zhang, Jin Du

и другие.

Frontiers in Medicine, Год журнала: 2025, Номер 12

Опубликована: Апрель 25, 2025

Liver diseases, including hepatitis, non-alcoholic fatty liver disease (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC), remain a major global health concern, with early accurate diagnosis being essential for effective management. Imaging modalities such as ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI) play crucial role in non-invasive diagnosis, but their sensitivity diagnostic accuracy can be limited. Recent advancements artificial intelligence (AI) have improved imaging-based assessment by enhancing pattern recognition, automating fibrosis steatosis quantification, aiding HCC detection. AI-driven techniques shown promise staging through US, CT, MRI, elastography, reducing the reliance on invasive biopsy. For steatosis, AI-assisted methods grading consistency, while detection characterization, AI models enhanced lesion identification, classification, risk stratification across modalities. The growing integration of into is reshaping workflows has potential to improve accuracy, efficiency, clinical decision-making. This review provides an overview applications imaging, focusing utility implications future diagnosis.

Язык: Английский

Advances in Pathogenesis and Therapeutics of Hepatobiliary Diseases II DOI Creative Commons
Jing‐Hua Wang

Biomedicines, Год журнала: 2025, Номер 13(4), С. 904 - 904

Опубликована: Апрель 8, 2025

Hepatobiliary diseases, including liver fibrosis, cirrhosis, hepatocellular carcinoma (HCC), and cholestatic disorders, pose significant global health challenges due to their complex pathogenesis limited treatment options [...]

Язык: Английский

Процитировано

0

Oxidative stress modulation in alcohol-related liver disease: From chinese botanical drugs to exercise-based interventions DOI Creative Commons
Yuting Zhu,

Yuqing Jia,

Enming Zhang

и другие.

Frontiers in Pharmacology, Год журнала: 2025, Номер 16

Опубликована: Апрель 25, 2025

Alcohol-related liver disease (ALD) is a chronic injury caused by long-term excessive alcohol consumption, with complex and multifaceted pathological mechanisms. Research indicates that oxidative stress (OS), inflammatory responses, lipid metabolic disturbances induced its metabolites are primary contributors to hepatocyte injury, positioning OS as key target in ALD treatment. The main non-pharmacological treatment for abstinence, while medical primarily relies on Western pharmacological interventions. However, recent research has increasingly highlighted the potential of Chinese botanical drugs improving histological features modulating signaling pathways associated ALD, underscoring therapeutic traditional herb medicine. Despite these promising findings, precise mechanisms effects extracts remain incompletely understood, side must be considered. Therefore, comprehensive analysis herbal essential optimize clinical administration ensure safe, effective This review focuses central theme, categorizing into six major groups—flavonoids, polyphenols, terpenoids, alkaloids, saponins, anthraquinones—all widely used provides an overview their characteristics actions context offering insights regulation exploring treatments ALD. Notably, physical exercise shares overlapping regulating OS. Combining two strategies could offer integrative though further needed confirm synergistic benefits applications.

Язык: Английский

Процитировано

0

Artificial intelligence in imaging for liver disease diagnosis DOI Creative Commons
Chenglong Yin, Huafeng Zhang, Jin Du

и другие.

Frontiers in Medicine, Год журнала: 2025, Номер 12

Опубликована: Апрель 25, 2025

Liver diseases, including hepatitis, non-alcoholic fatty liver disease (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC), remain a major global health concern, with early accurate diagnosis being essential for effective management. Imaging modalities such as ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI) play crucial role in non-invasive diagnosis, but their sensitivity diagnostic accuracy can be limited. Recent advancements artificial intelligence (AI) have improved imaging-based assessment by enhancing pattern recognition, automating fibrosis steatosis quantification, aiding HCC detection. AI-driven techniques shown promise staging through US, CT, MRI, elastography, reducing the reliance on invasive biopsy. For steatosis, AI-assisted methods grading consistency, while detection characterization, AI models enhanced lesion identification, classification, risk stratification across modalities. The growing integration of into is reshaping workflows has potential to improve accuracy, efficiency, clinical decision-making. This review provides an overview applications imaging, focusing utility implications future diagnosis.

Язык: Английский

Процитировано

0