AIMC Topic: Liver

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Deep Learning-aided H-MR Spectroscopy for Differentiating between Patients with and without Hepatocellular Carcinoma.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Among patients with hepatitis B virus-associated liver cirrhosis (HBV-LC), there may be differences in the hepatic parenchyma between those with and without hepatocellular carcinoma (HCC). Proton MR spectroscopy (H-MRS) is a well-established...

Liver MRI proton density fat fraction inference from contrast enhanced CT images using deep learning: A proof-of-concept study.

PloS one
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease worldwide, affecting over 30% of the global general population. Its progressive nature and association with other chronic diseases make...

Innovative machine learning approach for liver fibrosis and disease severity evaluation in MAFLD patients using MRI fat content analysis.

Clinical and experimental medicine
This study employed machine learning models to quantitatively analyze liver fat content from MRI images for the evaluation of liver fibrosis and disease severity in patients with metabolic dysfunction-associated fatty liver disease (MAFLD). A total o...

NAFLD progression in metabolic syndrome: a Raman spectroscopy and machine learning approach in an animal model.

The Analyst
Nonalcoholic fatty liver disease (NAFLD) is emerging as the leading cause of chronic liver disease in many regions, particularly in association with the rising prevalence of Metabolic syndrome (MetS), affecting more than 30% of the population worldwi...

AI-assisted intraoperative navigation for safe right liver mobilization in pure laparoscopic donor hepatectomy: an experimental multi-institutional validation study.

Scientific reports
Minimally invasive liver surgery (MILS) offers significant benefits but faces limited adoption due to its steep learning curve. This study explores the potential of artificial intelligence (AI) in assisting the performance of major MILS by providing ...

Multi-modal predictive modeling of schizophrenia severity: Leveraging liver function indicators and cognitive scores with random forest and SVM.

Psychiatry research. Neuroimaging
Schizophrenia is a complex neuropsychiatric disorder with cognitive deficits and systemic physiological disturbances, including emerging links to hepatic dysfunction via the gut-liver-brain axis. Despite growing evidence, the integration of liver fun...

FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms.

Journal of cancer research and clinical oncology
Precise liver segmentation is critical for accurate diagnosis and effective treatment planning, serving as a foundation for medical image analysis. However, existing methods struggle with limited labeled data, poor generalizability, and insufficient ...

Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-Net and Swin UNETR.

Scientific reports
Accurate segmentation of the liver parenchyma, portal veins, hepatic veins, and lesions from MRI is important for hepatic disease monitoring and treatment. Multi-phase contrast enhanced imaging is superior in distinguishing hepatic structures compare...

FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD.

BMC medical imaging
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a significant risk factor for liver cancer and cardiovascular diseases, imposing substantial social and economic burdens. Computed tomography (CT) scans are crucial for diagnosing NAFLD and ass...