Artificial intelligence-driven risk estimation, early detection, diagnostic characterisation and prognosis in hepatitis b-related hepatocellular carcinoma

Table 2 Artificial intelligence for early detection during surveillance

Ref.	Design, region	Population, aetiology and evidence category (n)	Endpoint	Approach	Validation	Performance	Calibration and explainability	Key limitation
Chaiteerakij et al[23]	Retrospective image cohort, Thailand	Patients undergoing hepatic ultrasonography; general HCC (26288 images, 5444 patients)	Focal lesion detection and malignancy	YOLOv5 object detection	Internal	Detection 84.8%; sensitivity for malignancy 97.0%; maintained at lesions ≤ 1 cm	Not reported	Image-level rather than outcome-level endpoint
Rhyou and Yoo[24]	Retrospective, single centre	Ultrasound dataset; general HCC	HCC classification	Wavelet-transform deep network (HCC-Net)	Internal	Accuracy: 96.2%; sensitivity: 97.6%; specificity 98.1%	Not reported	Single-centre dataset; no external validation
Brehar et al[25]	Retrospective	Ultrasound images; general HCC	Tumour vs cirrhotic parenchyma	CNN vs texture-based ML	Internal	CNN superior to conventional ML	Not reported	Small dataset; no clinical endpoint
Dana et al[26]	Retrospective multicentre	Cirrhosis under surveillance; mixed-aetiology	Risk stratification and early HCC detection	Ultrasound-based deep learning	Internal and multicentre	Improved stratification and detection of early HCC	Not reported in detail	Retrospective; no prospective outcome data
Schotten et al[27]	Retrospective, Europe	Chronic hepatitis B and C; mixed-aetiology	Early-stage HCC detection	GALAD (logistic regression; conventional comparator)	Internal	AUC around 0.95 in selected cohorts	Not applicable	Not an AI model; comparator instrument
Marsh et al[28]	Phase 3 biomarker validation, United States	Cirrhosis; mixed-aetiology	HCC detection within 12 months of diagnosis	GALAD (conventional comparator)	Prospective phase 3 design	Improved detection in the 12 months preceding clinical diagnosis	Not applicable	Not an AI model; comparator instrument
Johnson et al[29]	Retrospective, surveillance setting	Surveillance cohort; mixed-aetiology	HCC detection	Random forest applied to GALAD biomarkers	Internal	Outperformed original logistic GALAD in discrimination and F1 score	Not reported	Internal validation only
Tayob et al[30]	Community-based cohort, United States	Cirrhosis of multiple aetiologies; mixed-aetiology	Early HCC detection	HES V2.0 algorithm	External validation in community cohort	Validated in community-based cirrhosis cohort	Not reported in detail	Aetiology heterogeneous; limited HBV representation
Sun et al[31]	Case-control, China	Healthy, HBV-infected, cirrhotic and HCC participants; mixed-aetiology with HBV subgroup (374)	All-stage and early-stage HCC detection	Targeted bisulfite sequencing with ML	Internal	All-stage sensitivity 96.7% at specificity 93.7%; early-stage sensitivity 90.0%	Not reported	Case-control design including healthy controls; spectrum bias inflates apparent sensitivity
Xu et al[32]	Case-control, China	HCC patients and normal controls; general HCC (1098 HCC, 835 controls)	Diagnosis and prognosis	ctDNA methylation markers with prediction modelling	Internal	Diagnostic and prognostic models correlating with tumour burden, stage and treatment response	Not reported	Healthy comparator group; not a surveillance population

HCC: Hepatocellular carcinoma; YOLOv5: You only look once, version 5; CNN: Convolutional neural network; ML: Machine learning; GALAD: Gender, age, alpha-fetoprotein-L3 fraction, alpha-fetoprotein and des-gamma-carboxyprothrombin score; AUC: Area under the receiver operating characteristic curve; AI: Artificial intelligence; HES: Hepatocellular carcinoma early detection screening algorithm; HBV: Hepatitis B virus; ctDNA: Circulating tumour DNA.