Artificial intelligence in gastroenterology and hepatology: Status and challenges

Table 2 Summary of key studies on artificial intelligence-assisted radiology in hepatology fields

Ref.	Country	Disease studied	Design of study	Application	Number of cases	Type of machine learning algorithm	Outcomes (%)
							Accuracy	Sensitivity/Specificity
Ultrasound-based medical image recognition
Gatos et al[72], 2016	United States	Hepatic fibrosis	Retrospective	Classification of CLD	85 images: 54 healthy and 31 CLD	SVM	87	83.3/89.1
Gatos et al[73], 2017	United States	Hepatic fibrosis	Retrospective	Classification of CLD	124 images: 54 healthy and 70 CLD	SVM	87.3	93.5/81.2
Chen et al[74], 2017	China	Hepatic fibrosis	Retrospective	Classification of the stages of hepatic fibrosis in HBV patients	513 HBV patients with different hepatic fibrosis (119 S0, 164 S1, 88 S2, 72 S3, and 70 S4)	SVM, Naive Bayes, RF, KNN	82.87	92.97/82.50
Li et al[75], 2019	China	Hepatic fibrosis	Prospective	Classification of the stages of hepatic fibrosis in HBV patients	144 HBV patients	Adaptive boosting, decision tree, RF, SVM	85	93.8/76.9
Gatos et al[76], 2019	United States	Hepatic fibrosis	Retrospective	Classification of CLD	88 healthy individuals (88 F0 fibrosis stage images) and 112 CLD patients (112 images: 46 F1, 16 F2, 22 F3, and 28 F4)	CNNs	82.5	NA/NA
Wang et al[77], 2019	China	Hepatic fibrosis	Prospective	Classification of the stages of hepatic fibrosis in HBV patients	Training: 266 HBV patients (1330 images); Testing: 132 HBV patients (660 images)	CNNs	F4: 100; ≥ F3: 99; ≥ F2: 99	F4: 100.0/100.0; ≥ F3: 97.4/95.7; ≥ F2: 100.0/97.7
Kuppili et al[78], 2017	United States	MAFLD	Retrospective	Detection and characterization of FLD	63 patients: 27 healthy and 36 MAFLD	ELM, SVM	ELM: 96.75; SVM: 89.01	NA/NA
Byra et al[79], 2018	Poland	MAFLD	Retrospective	Diagnosis of the amount of fat in the liver	55 severely obese patients	CNNs, SVM	96.3	100/88.2
Biswas et al[80], 2018	United States	MAFLD	Retrospective	Detection and risk stratification of FLD	63 patients: 27 healthy and 36 MAFLD	CNNs, SVM, ELM	CNNs: 100; SVM: 82; ELM: 92	NA/NA
Cao et al[81], 2020	China	MAFLD	Retrospective	Detection and classification of MAFLD	240 patients: 106 healthy, 57 mild MAFLD, 67 moderate MAFLD, and 10 severe MAFLD	CNNs	95.8	NA/NA
Guo et al[82], 2018	China	Liver tumors	Retrospective	Diagnosis of liver tumors	93 patients with liver tumors: 47 malignant lesions (22 HCC, 5 CC, and 10 RCLM), and 46 benign lesions	DNNs	90.41	93.56/86.89
Schmauch et al[83], 2019	France	FLL	Retrospective	Detection and characterization of FLL	Training: 367 patients (367 images); Testing: 177 patients	CNNs	Detection: 93.5; Characterization: 91.6	NA/NA
Yang et al[84], 2020	China	FLL	Retrospective	Detection of FLL	Training: 1815 patients with FLL (18000 images); Testing: 328 patients with FLL (3718 images)	CNNs	84.7	86.5/85.5
CT/MRI-based medical image recognition
Choi et al[85], 2018	South Korea	Hepatic fibrosis	Retrospective	Staging liver fibrosis by using CT images	Training: 7461 patients: 3357 F0, 113 F1, 284 F2, 460 F3, 3247 F4; Testing: 891 patients: 118 F0, 109 F1, 161 F2, 173 F3, 330 F4	CNNs	92.1–95.0	84.6–95.5/89.9–96.6
He et al[86], 2019	United States	Hepatic fibrosis	Retrospective	Staging liver fibrosis by using MRI images	Training: 225 CLD patients; Testing: 84 patients	SVM	81.8	72.2/87.0
Ahmed et al[87], 2020	Egypt	Hepatic fibrosis	Retrospective	Detection and staging of liver fibrosis by using MRI images	37 patients: 15 healthy and 22 CLD	SVM	83.7	81.8/86.6
Hectors et al[88], 2020	United States	Liver fibrosis	Retrospective	Staging liver fibrosis by using MRI images	Training: 178 patients with liver fibrosis; Testing: 54 patients with liver fibrosis	CNNs	F1-F4: 85; F2-F4: 89; F3-F4: 91; F4: 83	F1-F4: 84/90; F2-F4: 87/93; F3-F4: 97/83; F4: 68/94
Vivanti et al[89], 2017	Israel	Liver tumors	Retrospective	Detection and segmentation of new tumors in follow-up by using CT images	246 liver tumors (97 new tumors)	CNNs	86	70/NA
Yasaka et al[90], 2018	Japan	Liver masses	Retrospective	Detection and differentiation of liver masses by using CT images	Training: 460 patients with liver masses (1068 images: 240 Category A, 121 Category B, 320 Category C, 207 Category D, 180 Category E); Testing: 100 images with liver masses: 21 Category A, 9 Category B, 35 Category C, 20 Category D, 15 Category E	CNNs	84	Category A: 71/NA; Category B: 33/NA; Category C: 94/NA; Category D: 90/NA; Category E: 100/NA
Ibragimov et al[91], 2018	United States	Liver diseases requiring SBRT	Retrospective	Prediction of hepatotoxicity after liver SBRT by using CT images	125 patients undergone liver SBRT: 58 liver metastases, 36 HCC, 27 cholangiocarcinoma, and 4 other histopathologies	CNNs	85	NA/NA
Abajian et al[92], 2018	United States	HCC	Retrospective	Prediction of HCC response to TACE by using MRI images	36 HCC patients treated with TACE	RF	78	62.5/82.1
Zhang et al[93], 2018	United States	HCC	Retrospective	Classification of HCC by using MRI images	20 patients with HCC	CNNs	80	NA/NA
Morshid et al[94], 2019	United States	HCC	Retrospective	Prediction of HCC response to TACE by using CT images	105 HCC patients received first-line treatment with TACE	CNNs	74.2	NA/NA
Nayak et al[95], 2019	India	Cirrhosis; HCC	Retrospective	Detection of cirrhosis and HCC by using CT images	40 patients: 14 healthy, 12 cirrhosis, 14 cirrhosis with HCC	SVM	86.9	100/95
Hamm et al[96], 2019	United States	Common hepatic lesions	Retrospective	Classification of common hepatic lesions by using MRI images	Training: 434 patients with common hepatic lesions; Testing: 60 patients with common hepatic lesions	CNNs	92	92/98
Wang et al[97], 2019	United States	Common hepatic lesions	Retrospective	Demonstration of a proof-of-concept interpretable DL system by using MRI images	60 common hepatic lesions patients	CNNs	NA	82.9/NA
Jansen et al[98], 2019	Netherlands	FLL	Retrospective	Classification of FLL by using MRI images	95 patients with FLL (125 benign lesions: 40 adenomas, 29 cysts, and 56 hemangiomas; and 88 malignant lesions: 30 HCC and 58 metastases)	RF	77	Adenoma: 80/78; Cyst: 93/93; Hemangioma: 84/82; HCC: 73/56; Metastasis: 62/77
Mokrane et al[99], 2020	France	HCC	Retrospective	Diagnosis of HCC in patients with cirrhosis by using CT images	Training: 106 patients: 85 HCC and 21 non-HCC; Testing: 36 patients: 23 HCC and 13 non-HCC	SVM, KNN, RF	70	70/54
Shi et al[100], 2020	China	HCC	Retrospective	Detection of HCC from FLL by using CT images	Training: 359 lesions: 155 HCC and 204 non-HCC; Testing: 90 lesions: 39 HCC and 51 non-HCC	CNNs	85.6	74.4/94.1
Alirr et al[101], 2020	Kuwait	Liver tumors	Retrospective	Segmentation of liver tumors	Training: 100 images with liver tumors;Testing: 31 images with liver tumors	CNNs	95.2	NA/NA
Zheng et al[102], 2020	China	Pancreatic cancer	Retrospective	Pancreas segmentation by using MRI images	20 patients with PDAC	CNNs	99.86	NA/NA
Radiomics
Liang et al[103], 2014	China	HCC	Retrospective	Prediction of recurrence for HCC patients who received RFA	83 patients with HCC receiving RFA as first treatment (18 recurrence and 65 non-recurrence)	SVM	82	67/86
Zhou et al[104], 2017	China	HCC	Retrospective	Characterization of HCC	46 patients with HCC: 21 low-grade (Edmondson grades I and II) and 25 high-grade (Edmondson grades III and IV)	Free-form curve-fitting	86.95	76.00/100.00
Abajian et al[105], 2018	United States	HCC	Retrospective	Prediction of response to intra-arterial treatment	36 patients undergone trans-arterial treatment	RF	78	62.5/82.1
Ibragimov et al[91], 2018	United States	Liver tumors	Retrospective	Prediction of hepatobiliary toxicity of SBRT	125 patients undergone liver SBRT: 58 metapatients, 36 HCC, 27 cholangiocarcinoma, and 4 other primary liver tumor histopathologies	CNNs	85	NA/NA
Morshid et al[94], 2019	United States	HCC	Retrospective	Prediction of HCC response to TACE	105 patients with HCC: 11 BCLC stage A, 24 BCLC stage B, 67 BCLC stage C, and 3 BCLC stage D	CNNs	74.2	NA/NA
Ma et al[106], 2019	China	HCC	Retrospective	Prediction of MVI in HCC	Training: 110 patients with HCC: 37 with MVI and 73 without MVI; Testing: 47 patients with HCC: 18 with MVI and 29 without MVI	SVM	76.6	65.6/94.4
Dong et al[107], 2020	China	HCC	Retrospective	Prediction and differentiation of MVI in HCC	Prediction: 322 patients with HCC: 144 with MVI and 178 without MVI; Differentiation: 144 patients with HCC and MVI	RF, mRMR	Prediction: 63.4; Differentiation: 73.0	Prediction: 89.2/48.4; Differentiation: 33.3/80.0
He et al[108], 2020	China	HCC	Prospective	Prediction of MVI in HCC	Training: 101 patients with HCC; Testing: 18 patients with HCC	LASSO	84.4	NA/NA
Schoenberg et al[109], 2020	Germany	HCC	Prospective	Prediction of disease-free survival after HCC resection	Training: 127 patients with HCC; Testing: 53 patients with HCC	RF	78.8	NA/NA
Zhao et al[110], 2020	China	HCC	Retrospective	Prediction of ER of HCC after partial hepatectomy	Training: 78 patients with HCC: 40 with ER and 38 without ER; Testing: 35 patients with HCC: 18 with ER and 17 without ER	LASSO	80.8	80.0/81.6
Liu et al[111], 2020	China	HCC	Retrospective	Prediction of progression-free survival of HCC patients after RFA and SR	RFA: Training: 149 HCC patients undergone RFA Testing: 65 HCC patients undergone RFA; SR: Training: 144 HCC patients undergone SR Testing: 61 HCC patients undergone SR	Cox-CNNs	RFA: 82.0; SR: 86.3	NA/NA
Chen et al[112], 2021	China	HCC	Retrospective	Prediction of HCC response to first TACE by using CT images	Training: 355 patients with HCC; Testing: 118 patients with HCC	LASSO	81	85.2/77.2

AI: Artificial intelligence; CLD: Chronic liver disease; SVM: Support vector machine; HBV: Hepatitis-B virus; RF: Random forests; KNN: K-nearest neighbor; CNN: Convolutional neural network; NA: Not available; MAFLD: Metabolic associated fatty liver disease; FLD: Fatty liver disease; ELM: Extreme learning machine; HCC: Hepatocellular carcinoma; CC: Cholangiocarcinoma; RCLM: Colorectal cancer liver metastases; DNN: deep neural network; FLL: Focal liver lesions; CLD: Chronic liver disease; SBRT: Stereotactic body radiation therapy; TACE: Transarterial chemotherapy; PDAC: Pancreatic ductal adenocarcinoma; RFA: Radiofrequency ablation; BCLC: Barcelona clinic liver cancer staging; MVI: Microvascular invasion; mRMR: Minimum redundancy maximum relevance; LASSO: Least absolute shrinkage and selection operator; ER: Early recurrence; SR: Surgical resection.