Role of artificial intelligence-based ocular biomarkers in hepatobiliary diseases: A scoping review

Table 1 Studies applying ocular biomarkers in hepatobiliary diseases

Ref.	Aim	Population	Model details	Performance	Implications	Study type
Xiao et al[12]	Detect hepatobiliary diseases from eye images	1000 individuals (multi-centre)	ResNet-101 on external and retinal images	Cirrhosis AUC: 0.90, HCC AUC: 0.93, NAFLD/Hepatitis AUC: 0.65–0.75	Supports non-invasive AI-based liver disease screening using eye images	AI imaging study
Babenko et al[14]	Predict systemic biomarkers (AST, albumin) from eye photos	Diabetic patients	Custom deep learning system on external eye photos	Accuracy improvement of 5%–20% over non-image models	Demonstrates the potential of ocular imaging to reflect liver function	AI imaging study
Kazankov et al[32]	Correlate scleral colour with bilirubin levels	Cirrhosis patients with jaundice	Smartphone imaging with colour analysis (non-AI)	Correlation ρ: 0.90 with serum bilirubin	Enables remote, non-invasive bilirubin monitoring	Clinical validation (non-AI)
Mariakakis et al[15]	Detect jaundice via scleral colour using a smartphone app	70 subjects (pilot study)	Smartphone app 'BiliScreen' with colour calibration	Sensitivity: 90%, Specificity: 97%, r = 0.89 with bilirubin	Feasible tool for at-home jaundice screening	Pilot study (AI with smartphone imaging)
Song et al[13]	Detect and grade Kayser-Fleischer rings in Wilson's disease	Database of 1850 corneal images	YOLO for detection, U-Net for segmentation, ResNet for grading	Accuracy > 95% (recall and specificity)	Facilitates early detection and monitoring of Wilson's disease	AI imaging study
Casanova-Ferrer et al[20]	Identify minimal hepatic encephalopathy via eye movements	Cirrhotic patients with and without minimal hepatic encephalopathy	Video-oculography with machine learning classification	56/177 eye movement metrics were significantly different in minimal hepatic encephalopathy	Provides a functional ocular biomarker for minimal hepatic encephalopathy diagnosis	Clinical diagnostic study (AI with VOG)
Li et al[33]	Predict systemic diseases from retinal fundus images	General population	Deep learning models on retinal fundus images	Demonstrated potential in detecting diseases, including hepatobiliary conditions	Highlights the utility of retinal imaging in systemic disease screening	AI imaging study

AI: Artificial intelligence; VOG: Video-oculography; AUC: Area under the curve; HCC: Hepatocellular carcinoma; AST: Aspartate aminotransferase; NAFLD: Non-alcoholic fatty liver disease; YOLO: You Only Look Once.