关键词:
artificial intelligence
liver imaging
AI in liver imaging
LI-RADS
2D
two-dimensional
3D
three dimensional
AI
Artificial intelligence
AUROC
area under the receiver operating characteristic curve
AUC
area under the curve
BD-LSTM
bidirectional long short-term memory
CAD
computer-aided detection/diagnosis
C index
concordance index
CEUS
contrast enhanced US
CLAIM
Checklist for Artificial Intelligence in Medical Imaging
CNN
convolutional neural network
CT
computed tomography
DCNN
deep convolutional neural network
DL
deep learning
DSC
dice similarity coefficient
DWT
discrete wavelet transform
EHR
electronic health record
FAD
food and drug administration
FLL
focal liver lesion
GDPR
General Data Protection Regulation
HCC
hepatocellular carcinoma
HIPPA
Health Insurance Portability and Accountability
HRI
hepatorenal index
IT
information technology
LI-RADS
Liver Imaging Reporting and Data System
LSN
liver surface nodularity
LSTM
long short-term memory
MASLD
metabolic dysfunction–associated steatotic liver disease
METAVIR
meta-analysis of histological data in viral hepatitis
MR
magnetic resonance
MRI
magnetic resonance imaging
MRI-PDFF
magnetic resonance imaging proton density fat fraction
ML
machine learning
MVI
microvascular invasion
NLP
natural-language processing
PACS
picture archiving and communication system
RECISR
response evaluation criteria in solid tumors
RF
radiofrequency
RNN
recurrent neural network
SSL
Self-Supervised Learning
SVM
support vector machine
SWE
shear wave elastography
TACE
transarterial chemoembolization
US
ultrasound
摘要:
Artificial intelligence (AI) has emerged as a transformative tool in liver imaging, offering enhanced diagnostic accuracy, efficiency, and reproducibility. The integration of machine learning and deep learning algorithms into radiological workflows has shown significant promise across a wide range of liver diseases. Key applications include automated liver segmentation on computed tomography (CT) and magnetic resonance imaging (MRI), enabling accurate liver volumetry and lesion localization. In metabolic dysfunction–associated steatotic liver disease, AI facilitates the detection and quantification of hepatic steatosis using advanced image analysis on ultrasound, CT, and MRI, providing a non-invasive alternative to biopsy. AI algorithms also demonstrate strong performance in detecting, classifying, and characterizing focal liver lesions such as hemangioma, focal nodular hyperplasia, hepatocellular carcinoma (HCC), and metastases, improving lesion conspicuity, standardizing reporting through LI-RADS, and reducing inter-observer variability. Beyond diagnosis, AI is increasingly applied for risk stratification and prognostication in HCC, integrating imaging, clinical, and laboratory data to predict tumor development, aggressiveness, treatment response, and survival outcomes. Despite these advances, the clinical implementation of AI in liver imaging faces notable challenges such as the need for data harmonization across scanners and institutions, rigorous validation in diverse patient populations, regulatory approval, and ethical considerations surrounding patient privacy, algorithmic bias, and transparency. Addressing these limitations through robust research, multi-center studies, and carefully designed clinical integration strategies is essential to safely and effectively harness AI’s potential. With continued development and validation, AI has the capacity to enhance diagnostic workflows, enable precision medicine, and ultimately improve patient outcomes in hepatolo