Artificial intelligence for hepatitis evaluation

Table 1 Hepatitis detection based on data mining

No.	Task	Algorithms	Sample size (type)	Evaluation index	Ref.
1	Predicting incidence of hepatitis A	ANN; ARIMA	N/A (CDC data)	ANN: Correlation coefficient 0.71; ARIMA: Correlation coefficient 0.66	[12]
2	Predicting incidence of hepatitis B	ARIMA; ElmanNN	486983 cases (data from health commission)	ARIMA: RMSE 0.94, MAE 0.81; ElmanNN: RMSE 0.89, MAE 0.70	[13]
3	Forecasting incidence of hepatitis B	Hybrid method (combing GM and BP-ANN)	10486959 cases (data from health ministry)	R 0.9495, RMSE 4.863 × 103, MAE 3.9704 × 104	[14]
4	Prediction of incidence of hepatitis E	ARIMA; SVM; LSTM	N/A (CDC data)	ARIMA: RMSE 0.022, MAE 0.018; SVM: RMSE 0.0204, MAE 0.0167; LSTM: RMSE 0.01, MAE 0.011	[15]
5	Automated classification of the different stages of hepatitis B	ADHB-ML-MFIS expert system	52 patients (serological data)	Overall accuracy: 0.922; No hepatitis accuracy: 1; Due to infection accuracy: 0.75; Acute HBV accuracy: 0.95; Chronic HBV accuracy: 0.91	[16]
6	Analyzing HBV infection from normal blood samples	Polynomial function; RBF	119 serum samples from HBV infected patients (Raman spectroscopy data)	Polynomial kernel (order-2): Quadratic programming/least squares: Accuracy 98%, precision 97%, sensitivity 100%, specificity 95%; RBF kernel (RBF sigma-2): Quadratic programming: accuracy 94%, precision 90%, sensitivity 100%, specificity 87%; RBF kernel (RBF sigma-2): Least squares: Accuracy 95%, precision 92%, sensitivity 100%, specificity 90%	[8]
7	Rapidly screening hepatitis B from non-hepatitis B	LSTM	1134 blood samples (Raman spectroscopy data)	Accuracy 97.32%, sensitivity 97.87%, specificity 96.77%, precision 96.84%	[17]
8	Finding undiagnosed patients with hepatitis C infection	Logistic regression; Gradient boosting trees; Gradient boosting trees with temporal variables; Stacked ensemble; Random forest	9721923 patients (data from the patient’s medical history)	The stacked ensemble had a specificity of 0.99 and precision of 0.97 at a recall level of 0.50	[19]
9	Predicting hepatitis C virus progression among veterans	CS Cox modellongitudinal Cox model; CS boosting modelLongitudinal-boosting model	72683 CHC individuals (VHA data)	CS Cox model: Concordance 0.746; Longitudinal Cox model: Concordance 0.764; CS boosting model: Concordance 0.758; Longitudinal-boosting model: Concordance 0.774	[20]
10	Forecasting response to IFN plus RIB treatment in HCV patients	ANN	300 patients (serological data)	The diagnostic accuracy rose from 52% (ANN 2) to 70% (ANN 6)	[21]

ANN: Artificial neural network; ARIMA: Autoregressive integrated moving average; CDC: Centers for Disease Control; RMSE: Root-mean-square error; MAE: Mean absolute error; GM: Grey model, BP-ANN: Back propagation artificial neural networks; SVM: Supporter vector machine; LSTM: Long-short time memory neural network; ADHB-ML-MFIS: Automated diagnosis of hepatitis B using multilayer Mamdani fuzzy inference; HBV: Hepatitis B virus; RBF: Gaussian radial basic function; CHC: Chronic hepatitis C virus; VHA: National Veterans Health Administration; IFN: Interferon; RIB: Ribavirin, HCV: Hepatitis C virus.

Table 2 Hepatitis or hepatitis associated lesion detection based on radiology

No.	Task	Algorithms (model)	Sample size (type)	Evaluation index	Ref.
1	Predicting clinical severity in AAH patients	Random forest; Convolutional neural network	69 cases (CT texture features)	Accuracy: 82.4% of RFE-RF in the test set; Accuracy: 70% of CNN in the test set	[26]
2	Assessing significant liver fibrosis by multiparametric ultrasomics data	Adaboost; Random forest; SVM (multiparametric ultrasomics)	144 HBV infected patients (multiparametric ultrasomics)	AUROC: 0.85 ± 0.01 of Adaboost, random forest, SVM in multiparametric ultrasomics including conventional ultrasomics, ORF and CEMF	[9]
3	Grading liver fibrosis	Inception-V3 network (transfer learning)	466 patients (multimodal ultrasound)	AUCs of TL in GM + EM reached 0.950, 0.932, and 0.930, respectively, for grading S4, ≥ S3, and ≥ S2an	[28]
4	Predicting cirrhosis	LASSO (radiomics nomogram)	144 cases of HBV patients (CT features and clinical factors)	AUROC: 0.915 in the training cohort, 0.872 in the validation cohort, overall correctly classified rate of 82.0%	[29]
5	Differentiating hepatic fibrosis’ grade	RFC (CTTA-based models); SVM (CTTA-based models)	30 fibrosis patients (CT texture features)	Train AUC 0.95 in RFC (model 1); Test AUC 0.90 in RFC (model 1); Train AUC 0.88 in SVM (model 2); Test AUC 0.76 in SVM (model 2)	[30]
6	Assessing liver fibrosis severity	A prototype convolutional neural network	558 cases (CT images)	AUCs were 0.82, 0.85, and 0.88 of VolL/VolS in diagnosing advanced fibrosis, cirrhosis, and decompensated cirrhosis in the whole study population	[31]
7	Staging liver fibrosis	Convolutional neural network	634 fibrosis patients (MR images and MR/virus)	AUCs were 0.84, 0.84, and 0.85 of the model full for diagnosing F4, ≥ F3, and ≥ F2 in the test set, respectively	[34]
8	Assessing liver fibrosis in chronic hepatitis B	Convolution neural network (DLRE)	398 HBV patients (shear wave elastography)	AUCs of DLRE 1.00, 0.99, and 0.99 for classifying F4, ≥ F3, and ≥ F2 in the training set and 0.97, 0.98, and 0.85 in the validation set	[35]
9	Diagnosing FNH from HCC in the non-cirrhotic liver	LASSO (radiomics nomogram)	156 patients (CT images and clinical factors)	Accuracy: 92.4% in the training set, 89.2% in the validation set	[38]
10	Diagnosing HCC	LASSO (radiomics signature)	211 patients (MR images)	AUROC: 0.861 in the training set, 0.810 in the validation set	[39]
11	Preoperative prediction of HCC grade	LASSO (combined model with clinical factors and radiomics signature)	170 HCC patients (MR images and clinical factors)	AUROC: 0.742, 0.786, and 0.800 based on T1WI images, T2WI images, and combined T1WI and T2WI images in the combined model	[41]
12	Predicting MVI risk in HBV-related HCC preoperatively	LASSO (radiomics nomogram)	304 HCC patients (CT images and AFP)	AUROC: 0.846 in the training set, 0.844 in the validation set	[43]
13	Preoperative prediction of MVI in HCC patients	LASSO (combined model)	157 HCC patients (CT images and clinical factors)	AUROC: 0.835 in the training dataset, 0.801 in the validation dataset	[44]
14	Predicting risk of HE complicated by hepatitis B related cirrhosis	LASSO (integrated model of radiomics and clinical features)	304 cirrhosis patients (CT images and clinical factors)	Accuracy: 0.93 in the training cohort, 0.83 in the testing cohort	[45]
15	Predicting liver failure in cirrhotic patients with HCC after major hepatectomy	LASSO (integrated radiomics-based mode)	101 HCC patients (MR images and clinical factors)	Accuracy: 0.802 in radiomics-based model	[47]

AAH: Alcohol-associated hepatitis; CT: Computed tomography; RFE-RF: Recursive feature elimination using random forest; CNN: Convolutional neural network; SVM: Supporter vector machine; HBV: Hepatitis B virus; AUROC: Area under the receiver operating curve; ORF: Original radiofrequency; CEMF: Contrast-enhanced micro-flow; AUC: Area under curve; TL: Transfer learning; GM: Gray scale modality; EM: Elastogram modality; LASSO: Least absolute shrinkage and selection operator; RFC: Random forest classifier; CTTA: Computed tomography texture analysis; VolL: Liver volume; VolS: Spleen volume; MR: Magnetic resonance; DLRE: Deep learning radiomics of shear wave elastography; FNH: Focal nodular hyperplasia; HCC: Hepatocellular carcinoma; MVI: Microvascular invasion; AFP: Alpha-fetoprotein; HE: Hepatic encephalopathy.