Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Table 1 Artificial intelligence applications in gastroenterology: Prevention

Ref.	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Leung et al[27]	Laboratory results, clinicopathological parameters	Several	64238/25330 patients	Risk of gastric cancer development following H.pylori eradication	0.53-0.972,6, 59.3-98.13,6, 51.5-93.64,6
Nakahira et al[28]	Laboratory results, clinicopathological parameters, endoscopic images	CNN	7826/454 patients	Stratify risk of gastric cancer development	---
Taninaga et al[29]	Laboratory results, clinicopathological parameters, endoscopic images	CART	1144/287	Prediction of future gastric cancer	63.4-94.81,6, 0.736-0.8742,6
Goshen et al[31]	Laboratory results, clinicopathological parameters	DT, RF, GB	688 flagged patients	High risk of CRC development	----

¹Accuracy (%).

²Area under the receiver operating curve or c-index.

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. CART: Classification and regression tree; CNN: Convolutional neural network; CRC: Colorectal cancer; DT: Decision tree; GB: Gradient boosting; RF: Random forest.

Table 2 Artificial intelligence applications in gastroenterology: Diagnosis

Ref.	Diagnostic Modality	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Takiyama et al[33]	Esophago-gastro-duodenoscopy imaging	CNN	1750/4357	Anatomical classification among larynx, esophagus, stomach, and duodenum	0.99-1.002,7
Pace et al[34]	Laboratory results, clinicopathological parameters	ANN	159 patients	Diagnosis of gastroesophageal reflux disease	67.86-1001,6
de Groof et al[35]	Esophageal endoscopic images	DNN	1247/2976/807/807 patients	Classification of malignant from nondysplastic Barret’s esophagus	88.21,6, 87.5-88.81,7, 87.63,6, 90.0-92.53,7, 88.64,6, 82.5-87.54,7
van der Sommen et al[36]	White-light endoscopic imaging	SVM	44 patients with Barret’s esophagus	Diagnosis of early neoplastic lesions	Per image: 62-903,6, 65-904,6, Per patient: 52-1003,6, 74-964,6
Struyvenberg et al[37]	Volumetric laser endomicroscopy imaging	Several	29 patients with Barret’s esophagus	Diagnosis of neoplastic lesions	0.83-0.942,6
Swager et al[38]	Volumetric laser endomicroscopy imaging	Several	60 images	Diagnosis of neoplastic lesions	0.89-0.952,6
Kumagai et al[39]	Endocytoscopic imaging	CNN	4715/15207	Diagnosis of esophageal squamous cell carcinoma	90.91,7, 0.72-0.902,7, 39.4-46.43,7, 98.2-98.44,7
Zheng et al[40]	Endoscopic images	CNN	1507/452 patients	Diagnosis of H.pylori infection	84.5-93.81,6, 0.93-0.972,6, 81.4-91.63,6, 90.1-98.64,6
Nakashima et al[41]	Endoscopic images	CNN	162/60 patients	Diagnosis of H.pylori infection	0.66-0.962,6
Itoh et al[42]	Endoscopic images	CNN	149/30 images	Diagnosis of H.pylori infection	0.9562,6, 86.73,6, 86.74,6
Shichijo et al[43]	Endoscopic images	CNN	32308/114817	Diagnosis of H.pylori infection	83.1-87.71,7, 81.9-88.93,7, 83.4-87.44,7
Kanesaka et al[45]	NBI	SVM	126/81 NBI images	Diagnosis of gastric cancer	96.31,6, 96.73,6, 95.04,6
Hirasawa et al[46]	Endoscopic images	CNN	13584/22967	Diagnosis of gastric cancer	92.23,7
Zhu et al[47]	Laboratory results, clinicopathological parameters, cancer biomarkers	GB/DT	496/213 patients	Diagnosis of gastric cancer	85.91,5, 831,6, 0.912,6, 883,5, 873,6, 83.44,5, 84.14,6
Tenório et al[48]	Laboratory results, clinicopathological parameters	Several	178/38	Diagnosis of celiac disease	71.5-801,6, 0.71-0.842,6, 69-823,6, 67-804,6
Caetano Dos Santos et al[49]	Endomysial autoantibody test for IgA-class antibodies images	SVM	2597 images (training:validation = 7:3)	Diagnosis of celiac disease	96.8-98.851,6, 82.84-98.913,6, 98.81-99.404,6
Hujoel et al[50]	Laboratory results, clinicopathological parameters	Several	408 undiagnosed patients	Diagnosis of celiac disease	0.49-0.532,6
Manandhar et al[51]	Gut microbiome data	RF	1429 fecal 16S metagenomic data subjects	Diagnosis of IBD	0.80-0.822,6
Wei et al[52]	Single nucleotide polymorphisms data	Several	60828 samples	Classifification of CD and UC	0.782-0.8662,6
Mossotto et al[53]	Capsule endoscopy, histologic imaging	SVM	239/487 pediatric patients	Classifification of CD, UC, and unclassified IBD	71-82.71,5, 0.78-0.872,5, 83.31,7, 83-853,7
Xia et al[58]	Capsule endoscopy imaging	CNN	697/1007 patients, 822590/2013657, images	Classification among different types of gastric lesions	77.1-861,7, 0.80-0.902,7, 96.2-1003,7, 56.5-76.24,7
Seguí et al[59]	Capsule endoscopy imaging	CNN	50 videos	Classification of small bowel mobility events	961,6
Park et al[60]	Capsule endoscopy imaging	CNN	139 videos, 200000 images (training:validation:test = 6:2:2)	Small bowel lesion identification	80.29-98.341,6, 0.9992,5, 0.9982,6,7
Hwang et al[61]	Capsule endoscopy imaging	CNN	7556/57607 images	Classification of hemorrhagic and ulcerative lesions	96.62-96.831,7, 95.07-97.613,7, 96.04-98.184,7
Otani et al[62]	Capsule endoscopy imaging	DNN	167/407 patients	Classification among different types of small bowel lesions	0.950-0.9962,6, 0.884-0.9282,7
Yuan et al[63]	Capsule endoscopy imaging	SVM	20 patients, 340 images (training:validation = 8:2)	Diagnosis of peptic ulcers	92.651,6, 94.123,6, 91.184,6
Karargyris et al[64]	Capsule endoscopy imaging	SVM	80 frames	Diagnosis of peptic ulcers	753,6, 73.34,6
He et al[65]	Capsule endoscopy imaging	CNN	11 patients, 440000 images	Diagnosis of intestinal hookworms	88.51,6, 0.8952,6, 84.63,6, 88.64,6
Leenhardt et al[66]	Capsule endoscopy imaging	CNN	600/600 images	Diagnosis of gastrointestinal angiectasia	1003,6, 964,6
Zhou et al[67]	Capsule endoscopy imaging	CNN	21 videos	Diagnosis of celiac disease	1003,6, 1004,6
Yamada et al[68]	Colon capsule endoscopy imaging	CNN	15933/47847	Diagnosis of colorectal neoplasias	83.97, 0.9022,7, 793,7, 874,7
Wang et al[69]	Colonoscopy imaging	CNN	5545 images/271137 images/6127 images/1387 videos/547 videos	Identification of colorectal polyps	0.9842,7, 88.24-1003,7, 95.40-95.922,7
Misawa et al[70]	Colonoscopy imaging	CNN	411/35 short videos	Identification of colorectal polyps	76.51,6, 0.872,6, 903,6, 63.34,6
Urban G et al[71]	Colonoscopy imaging	CNN	8641 images/207 videos	Identification of colorectal polyps	96.41,7, 0.9912,7
Ozawa et al[72]	Colonoscopy imaging	CNN	20431/70777 images	Identification of colorectal polyps, Classification of colorectal polyps	90-973,7, 47-981,7
Mori et al[73]	NBI and methylene blue staining images	SVM	466 diminutive polyps	Classification of diminutive rectosigmoid adenomas	NPV(%): 93.7-96.5
Tischendorf et al[74]	NBI	SVM	209 colorectal polyps	Classification of colorectal polyps	903,6, 70.24,6
Gross et al[75]	NBI	SVM	434 colorectal polyps	Classification of small colorectal polyps	93.11,6, 95.03,6, 90.34,6
Kominami et al[76]	NBI	SVM	118 colorectal polyps	Classification of colorectal polyps	93.21,6, 93.03,6, 93.34,6
Misawa et al[77]	NBI endocytoscopy	SVM	979/100 endocytoscopy, images	Classification of colorectal polyps	901,6, 84.53,6, 97.64,6
Takeda et al[78]	NBI endocytoscopy	SVM	5543/200 endocytoscopy, images	Diagnosis of invasive CRC	94.11,6, 89.43,6, 98.94,6
Chen et al[79]	NBI	CNN	2157/2847	Classification neoplastic from hyperplastic polyps	96.33,7, 78.14,7, NPV(%): 91.57
Komeda et al[80]	NBI	CNN	1200/600 images	Classification of adenomatous from non-adenomatous polyps	75.11,6
Byrne et al[81]	NBI	CNN	223/407 videos	Classification of adenomas from hyperplastic polyps	941,7, 0.952,7, 983,7, 834,7, NPV(%): 977

¹Accuracy (%).

²Area under the receiver operating curve or c-index.

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. ANN: Artificial neural network; CD: Chron’s disease; CNN: Convolutional neural network; CRC: Colorectal cancer; DNN: Deep neural network; DT: Decision tree; GB: Gradient boosting; IBD: Inflammatory bowel disease; NBI: Narrow-band imaging; NPV: Negative predictive value; RF: Random forest; SVM: Support vector machine; UC: Ulcerative colitis.

Table 3 Artificial intelligence applications in gastroenterology: Treatment

Ref.	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Rogers et al[86]	Data from baseline impedance, nocturnal baseline impedance, and acid exposure time	DT	335 patients	Prediction of treatment response with proton pump inhibitors for patients with gastroesophageal reflux disease	0.31-0.9382,6
Zhu et al[87]	Endoscopic images	CNN	790/2037 images	Invasion of gastric cancer at the mucosa and submucosa layers of the stomach	89.161,7, 0.942,7, 76.473,7, 95.564,7
Kubota et al[88]	Endoscopic images	DNN	800/90 images	Invasion depth of gastric cancer	64.71,6
Yamashita et al[89]	Hematoxylin and eosin-stained WSI	DNN	100/156/4847	Identificication of CRC microsatellite instability	0.9312,6, 0.7792,7, 763,7, 66.64,7
Ichimasa et al[90]	Laboratory results, clinicopathological parameters	SVM	590/1007	Prediction of lymph node metastasis status	691,7, 0.8212,7, 1003,7, 664,7
Levi et al[91]	Laboratory results, clinicopathological parameters	RFE	14620 patients	Prediction of the need for transfusion following GIB	50.21-74.881,6, 0.7858-0.81412,6, 69.17-92.773,6, 35.02-79.824,6
Chu et al[92]	Laboratory results, clinicopathological parameters	Several	122/67 patients	Prediction of the source of GIB	69.7-94.31,6, 0.658-0.9992,6, 90.1-98.03,6, 89-1004,6
				Prediction of the need for blood resuscitatio	64.7-94.11,6, 0.381-0.9932,6, 90.3-93.93,6, 18.4-95.54,6
				Prediction of the need for emergent endoscopy	62.7-83.31,6, 0.404-0.9132,6, 80.1-89.13,6, 13.8-85.74,6
				Prediction of disposition	58.4-89.71,6, 0.324-0.9722,6, 81.9-92.93,6, 18.4-90.94,6
Das et al[93]	Laboratory results, clinicopathological parameters	ANN	194/1936/2007 patients	Prediction of major stigmata of recent hemorrhage	891,3,4,6, 771,7, 963,7, 634,7
				Prediction of the need for emergent endoscopy	811,3,6, 611,7, 943,6, 824,6, 484,7
Augustin et al[94]	Laboratory results, clinicopathological parameters	CART	164/1037 patients	Stratification of risk of rebleeding and mortality following acute variceal hemorrhage	0.81-0.832,7
Loftus et al[95]	Laboratory results, clinicopathological parameters	ANN	103/44 patients	Prediction of severe lower GIB	0.9792
				Prediction of the need for surgical intervention	0.9542,6
Ayaru et al[96]	Laboratory results, clinicopathological parameters	GB	170/1307	Prediction of severe lower GIB	781,6, 831,7
				Prediction of recurrent bleeding	881,6, 881,7
				Prediction of the need for intervention	881,6, 911,7

¹Accuracy (%).

²Area under the receiver operating curve or c-index.

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. ANN: Artificial neural network; CART: Classification and regression tree; CNN: Convolutional neural network; CRC: Colorectal cancer; DT: Decision tree; DNN: Deep neural network; GB: Gradient boosting; GIB: Gastrointestinal bleeding; RFE: Recursive feature elimination; WSI: Whole-slide image.

Table 4 Artificial intelligence applications in gastroenterology: Prognosis

Ref.	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Yang et al[97]	Laboratory results, immunomarkers, clinicopathological parameters	SVM	319/164 patients	Distant metastasis of oesophageal squamous cell carcinoma following surgery	69.5-80.11,6, 44.7-67.23,6, 81.6-97.74,6
Sato et al[98]	Laboratory results, clinicopathological parameters, tumor characteristics	ANN	395 patients (training:validation:test = 53:27:20)	1-year and 5-year survival of patients with esophageal cancer following surgery	0.883-0.8842,7, 78.1-80.73,7, 84.7-86.54,7
Zhou et al[99]	Laboratory results, clinicopathological parameters, tumor characteristics	Several	2012 patients (training:validation = 8:2)	Recurrence of gastric cancer following surgery	0.790-0.9622,5, 0.771-0.7952,6
Peng et al[100]	Meteorological data	ANN	901 patients	Variations of onset and relapse of IBDs	----
Hardalaç et al[101]	Clinicopathological parameters, treatment data	ANN	129 patients (training:validation:test = 80:10:10)	Prediction of mucosal remission for CD patients treated with azathioprine	58.1-79.11,6, 0.527-0.8832,6
Takayama et al[102]	Clinicopathological parameters, treatment data	ANN	54/36 patients	Prediction of the need for operation for UC patients treated with cytoapheresis	963,6, 974,6
Lyles et al[103]	Laboratory results, clinicopathological parameters	CART	884 patients	Prediction of in-hospital mortality of upper GIB in cirrhotic patients	----
Grossi et al[104]	Laboratory results, clinicopathological parameters	ANN	807 patients	30-d mortality of patients with non-variceal upper GIB	81.2-89.01,6, 0.872,6, 81.5-93.33,6, 80.9-84.74,6
Rotondano et al[105]	Laboratory results, clinicopathological parameters	ANN	2380 patients	30-d mortality of patients with non-variceal upper GIB	96.81,6, 0.952,6, 83.83,6, 97.54,6
Shi et al[106]	CT radiomics	Several	124/35 patients	Prediction of the presence of RAS and BRAF mutations in CRC	ANN: 871,5, 711,6, 0.90-0.952,5, 0.792,6
Kang et al[107]	Laboratory results, immunomarkers, clinicopathological parameters, tumor characteristics	LASSO	221/95 patients	Prediction of lymph node metastasis status in operated patients for T1 CRC	0.7952,5, 0.7652,6

¹Accuracy (%).

²Area under the receiver operating curve or c-index.

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. ANN: Artificial neural network; CART: Classification and regression tree; CD: Chron’s disease; CRC: Colorectal cancer; CT: Computed tomography; GIB: Gastrointestinal bleeding; IBD: Inflammatory bowel disease; LASSO: Least absolute shrinkage and selection operator; SVM: Support vector machine; UC: Ulcerative colitis.

Table 5 Artificial intelligence applications in hepatology: Prevention

Ref.	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Goldman et al[108]	National database of routine annual health check-ups	DT-based	12019 patients	Risk of NAFLD and cirrhosis	84.50-85.731,6, 0.7740-0.84862,6
Yip et al[109]	Laboratory results, clinicopathological parameters	Several	500/422	Identify patients with NAFLD	0.87-0.902,5, 0.78-0.882,6, 55.48-94.523,5, 51.69-92.373,6, 58.47-91.534,5, 50.99-90.464,6
Ma et al[110]	Laboratory results, clinicopathological parameters	Several	10508 patients (training:validation = 9:1)	Identify patients with NAFLD	49.47-82.921,6, 20.2-68.03,6, 54.4-94.64,6
Sowa et al[111]	Laboratory results, clinicopathological parameters	Several	126 morbidly obese patients (training:validation = 9:1)	Fibrosis in NAFLD patients	791,6, 30.8-60.03,6, 77.0-92.24,6
Canbay et al[112]	Laboratory results, clinicopathological parameters	EFS	164/122 obese patients	Classification of NAFLD and NASH	0.73392,5, 0.70282,6
Fialoke et al[113]	National database of routine annual health check-ups	Several	108139 patients (training:validation = 4:1)	Classification among healthy, NAFLD, and NASH	77.2-79.71,6, 0.842-0.8762,6, 74.5-77.43,6
Sowa et al[114]	Data from biochemical and enzyme-linked immunosorbent assays	Several	133 patients (training:validation = 9:1)	Classification of NAFLD and ALD	DT: 89.02-95.11,6, 74.19-94.123,6, 96.08-98.044,6, RF: 0.8932-0.98462,6, SVM: 0.9058-0.91182,6
Wei et al[115]	Laboratory results, clinicopathological parameters	GB	576 HBV patients, (training:validation = 7:3), 3687 HCV patients	Classification of fibrosis/cirrhosis in HBV patients	0.904-0.9742,5, 0.871-0.9182,6, 79-883,5, 78-843,6, 86-924,5, 854,6
				Classification of fibrosis/cirrhosis in HCV patients	0.797-0.8492,7
Wang et al[116]	Laboratory results, clinicopathological parameters	ANN	226/1136/1167 HBV patients	Classification of significant fibrosis	0.8832,5, 0.8842,6, 0.9202,7
Raoufy et al[117]	Laboratory results, clinicopathological parameters	ANN	86/58 HBV patients	Classification of liver cirrhosis	91.381,6, 0.8982,6, 87.53,6, 924,6
Piscaglia et al[118]	Laboratory results, clinicopathological parameters	ANN	414/96 HCV patients	Classification of significant fibrosis	45.8-86.51,6, 0.872,5, 0.932,6, 30.4-1003,6, 30.1-98.64,6
Hashem et al[119]	Laboratory results, clinicopathological parameters	Several	22690/16877 HCV patients	Classification of significant fibrosis	66.3-84.41,6, 0.73-0.762,6
Ioannou et al[120]	Clinical/laboratory data extracted directly from electronic health records	DNN	48151 patients with HCV-related cirrhosis (training:validation = 9:1)	HCC development in HCV cirrhosis	0.759-0.8062,6
Emu et al[121]	Laboratory results, clinicopathological parameters	Several	1385 patients HCV (training:validation = 4:1)	Stage of liver cirrhosis	97.228-97.8311,6

¹Accuracy (%).

²Area under the receiver operating curve or c-index.

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. ALD: Alcoholic liver disease; ANN: Artificial neural network; DNN: Deep neural network; DT: Decision tree; EFS: Ensemble feature selection; GB: Gradient boosting; HBV: Hepatitis B; HCC: Hepatocellular carcinoma; HCV: Hepatitis C; NAFLD: Non-alcoholic fatty liver disease; NASH: Non-alcoholic steatohepatitis RF: Random forest; SVM: Support vector machine.

Table 6 Artificial intelligence applications in hepatology: Diagnosis

Ref.	Diagnostic Modality	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Choi et al[122]	CT imaging	CNN	7461/4216/2987/1727 patients	Liver fibrosis staging (F0-F4)	83.11,5, 80.81,6, 74.4-80.21,7
				Classification among significant fibrosis, advanced fibrosis, and cirrhosis	92.1-95.01,6,7, 0.95-0.972,6,7, 84.6-95.53,6,7, 89.9-96.64,6,7
Kuppili et al[123]	US imaging	ELM, SVM	63 patients	Diagnosis of FLD	ELM: 81.7-92.41,6, 0.81-0.922,6, 85.10-91.303,6, 78.52-92.104,6, SVM: 76.14-86.421,6, 0.74-0.862,6, 76.80-88.203,6, 74.52-86.304,6
Gatos et al[124]	US shear wave elastography imaging	SVM	126 patients	Classification of chronic liver disease from healthy patients	87.31,6, 0.872,6, 93.53,6, 81.24,6
Chen et al[125]	Real-time tissue elastography imaging, age, sex	Several	513 patient (training:validation = 3:1)	Classification of liver fibrosis	80.44-82.871,6, 79.67-92.973,6, 46.25-82.504,6
Matake et al[126]	Clinicopathological parameters, CT imaging	ANN	120 patients	Classification among four types of focal liver lesions	0.9612,6
Oestmann et al[127]	Multiphasic MRI scans	CNN	150/10 patients	Classification of HCC and non-HCC lesions	94.11,5, 87.31,6, 0.9122,6
					For HCC: 92.73,6, 82.04,6
					For non-HCC: 82.03,6, 92.74,6
Kim et al[128]	MRI scans	CNN	4555,6/547 patients	HCC detection	0.972,6, 943,6, 994,6, 0.902,7, 873,7, 934,7
Cucchetti et al[129]	Laboratory results, clinicopathological parameters, radiological data, histological data	ANN	175/75 patients	MVI	0.922,5, 91.01,6
				Histopathological Grade	0.942,5, 93.31,6
Urman et al[130]	Metabolomic and proteomic analyses of bile	Several	139 patients	Classification of CCA and pancreatic adenocarcinoma	0.98-1.002,6, 88-94.13,6, 92.3-1004,6
Negrini et al[131]	Plasma bile acids profiles	Several	112 patients (training:validation = 4:1)	Classification of CCA and benign biliary disease	68.2-86.41,6, 0.77-0.952,6, 64-793,6, 63-1004,6
Logeswaran[132]	MRCP	MLP	55/5937 images	CCA diagnosis	92.8-96.31,6, 83.64-90.141,7

¹Accuracy (%).

²Area under the receiver operating curve or c-index.

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. ANN: Artificial neural network; CCA: Cholangiocarcinoma; CNN: Convolutional neural network; CT: Computed tomography; ELM: Extreme learning machine; FLD: Fatty liver disease; HCC: Hepatocellular carcinoma; MLP: Multi-layer perceptron; MRCP: Magnetic resonance cholangiopancreatography MRI: Magnetic resonance imaging; MVI: Microvascular invasion; SVM: Support vector machine; US: Ultrasound.

Table 7 Artificial intelligence applications in hepatology: Treatment

Ref.	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Wübbolding et al[133]	Analyze soluble immune markers	Several	28/497 HBV patients	Prediction of early virological relapse	0.73-0.892,6, 0.59-0.672,7
Haga et al[134]	WGS of HCV	Several	86/87 HCV patients	Classification of HCV variants resistant to antiviral drugs	0.5-0.9372,5, 0.597-0.9542,6
Bedon et al[135]	DNA methylation profiling	RF-based	300/74 HCC specimens	6-mo progression-free survival	67.1-80.61,5, 64.8-80.21,7
Tsilimigras et al[137]	Laboratory results, clinicopathological parameters, tumor characteristics	CART	976 HCC patients	Determining factors of prognostic weigh preoperatively within the BCLC staging system	---
Tsilimigras et al[139]	Laboratory results, clinicopathological parameters, tumor characteristics	CART	1146 CCA patients	Determining factors of prognostic weigh preoperatively	---
Jeong et al[140]	Laboratory results, clinicopathological parameters	DNN	1421/2347	Intrahepatic CCA susceptible to adjuvant therapy following resection	0.842,5, 0.782,7
Shao et al[141]	Clinicopathological parameters	ANN	288 CCA patients (training:validation = 8:2)	Predict early occlusion following bilateral plastic stent placement	0.96482,5, 0.95442,6

¹Accuracy (%).

²Area under the receiver operating curve or c-index.

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. ANN: Artificial neural network; BCLC: Barcelona clinic liver cancer; CART: Classification and regression tree; CCA: Cholangiocarcinoma; DNN: Deep neural network; HCC: Hepatocellular carcinoma; HCV: Hepatitis C; RF: Random forest; WGS: Whole-genome sequencing.

Table 8 Artificial intelligence applications in hepatology: Prognosis

Ref.	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance
Hong et al[142]	Laboratory results, clinicopathological parameters	ANN	197 HBV patients (training:validation = 4:1)	Development of esophageal varices in HBV cirrhosis	87.821,6, 93.753,6, 71.704,6
Dong et al[143]	Laboratory results, clinicopathological parameters	RF	238/1097	Identification of esophageal varices	0.842,5, 0.822,7
				Classification of esophageal varices requiring treatment	0.742,5, 0.752,7
Ho et al[144]	Laboratory results, clinicopathological parameters, surgery parameters	ANN, DT	427, 354, and 297 patients for 1-, 3-, and 5-year survival (training:validation = 8:2)	1-, 3-, and 5-year disease-free survival	ANN: 0.963-0.9892,5, 93.5-96.33,5, 91.6-97.94,5, 0.774-0.8642,6, 70.0-78.73,6, 54.2-92.74,6
				Following surgical resection
					DT: 0.675-0.8252,5, 19.6-94.83,5, 45.8-97.94,5, 0.561-0.7182,6, 0-88.53,6, 37.5-96.44,6
Shi et al[145]	Laboratory results, clinicopathological parameters, tumor characteristics	ANN	22926 patients	5-year survival following surgical resection	96.571,6, 0.8852,6, 97.431,7, 0.8712,7, 74.233,7,
Shi et al[146]	Laboratory results, clinicopathological parameters, surgery parameters	ANN	22926 hepatectomies	In-hospital mortality following surgical resection	97.281,6, 0.842,6, 95.931,7, 0.822,7, 78.403,7, 94.574,7
Chiu et al[147]	Laboratory results, clinicopathological parameters, tumor characteristics	ANN	434, 341, and 264 patients for 1-, 3-, and 5-year survival, (training:validation = 8:2)	1-, 3-, and 5-year overall survival, following surgical resection	98.5-99.51,5, 0.980-0.9932,5, 99.7-1003,5, 96.2-99.24,5, 72.1-85.11,6, 0.798-0.8752,6, 71.4-88.63,6, 50.0-82.14,6
Qiao et al[148]	Laboratory results, clinicopathological parameters, tumor characteristics	ANN	362/1816/1047 patients	Survival following surgical resection	0.8552,5, 80.003,5, 73.404,5, 0.8322,6, 78.673,6, 75.704,6, 0.8292,7, 77.423,7, 78.084,7
Liu et al[149]	Laboratory results, data from immunochemistry of peripheral blood mononuclear cells, tumor characteristics	GB survival classifier	136/566/1057	Risk of HCC-related death	0.8442,5, 0.8272,6, 0.8062,7
Zhong et al[150]	ALBI/CTP stage	ANN	319 / 617 / 1247	Survival of patients treated with chemoembolization and sorafenib	ALBI-based: 0.7162,7, 0.8232,7
					CTP-based: 0.7792,7, 0.6932,7
Divya and Radha[152]	Laboratory results, clinicopathological parameters, tumor characteristics	APO, SVM, RF	152 patients	Recurrence following RFA	95.51,6, 95.13,6, 95.84,6
Yamashita et al[153]	Hematoxylin and eosin-stained WSI	CNN	299 / 536/1987 WSIs	Recurrence following Surgical Resection	0.7242,6, 0.6832,7
Liang et al[154]	Laboratory results, clinicopathological parameters	SVM	83 patients	Recurrence following RFA	73-821,6, 0.60-0.692,6, 77-863,6, 73-824,6
Eaton et al[155]	Laboratory results, clinicopathological parameters	GB-based	509/278 patients with primary sclerosing cholangitis	Classify risk of primary sclerosing cholangitis-related complications	0.962,6, 0.902,7
Andres et al[156]	Laboratory results, clinicopathological parameters, donor characteristics	PSSP system	2769 patients	Survival following transplantation for primary sclerosing cholangitis	----
Rodriguez-Luna et al[157]	Genotyping data from microsatellite mutations/deletions	ANN	19 transplated patients	Post-transplant HCC recurrence	89.51,6
Lau et al[158]	Laboratory results, clinicopathological parameters, donor characteristics	ANN, RF	90/90 transplants	Graft failure/primary nonfunction	ANN: 0.734-0.8352,6
					RF: 0.787-0.8182,6
				3-mo graft failure	ANN: 0.5592,6, R6: 0.7152,6
Briceño et al[160]	Laboratory results, clinicopathological parameters, surgical parameters, donor characteristics	ANN	1003 liver transplants	3-mo graft failure	0.806-0.8212,6

¹Accuracy (%).

²Area under the receiver operating curve or c-index.

³Sensitivity (%).

⁴Specificity (%).

⁵Training.

⁶Internal validation.

⁷External validation/testing. ALBI: Albumin-bilirubin; ANN: Artificial neural network; APO: Artificial plant optimization; CNN: Convolutional neural network; CTP: Child–Turcotte–Pugh; DT: Decision tree; GB: Gradient boosting; HBV: Hepatitis B; HCC: Hepatocellular carcinoma; PSSP: Patient-specific survival prediction; RF: Random forest; RFA: Radiofrequency ablation; SVM: Support vector machine; WSI: Whole-slide image.