Artificial intelligence in nonalcoholic fatty liver disease

Mahzari, Ali

doi:10.1186/s43066-022-00224-w

Egyptian Liver Journal

Table 1 Important studies defining AI in NAFLD

From: Artificial intelligence in nonalcoholic fatty liver disease

Study	Country	Study cohort	Diagnostic method	AI classifier	Development cohort (n)	Validation cohort (n)	Validation methods	Sensitivity	Specificity	TP	FP	TN	FN
Aim: AI-assisted ultrasonography to diagnose NAFLD					NAFLD/total, % Steatosis	NAFLD/total, % Steatosis
Kuppili et al.	Portugal	Retrospective	Liver biopsy (not defined)	ELMa, SVM	36/63	N/A	k-fold cross-validation	0.913	0.921	33	2	25	3
Kuppili et al.	Portugal	Retrospective	Liver biopsy (not defined)	ELMa, SVM	N/A	N/A	k-fold cross-validation	0.913	0.921	33	2	25	3
Byra, et al.	Poland	Prospective	Liver biopsy (> 5% hepatocyte steatosis)	CNN	38/55	N/A	5-fold cross-validation	1	0.882	38	3	15	0
Byra, et al.	Poland	Prospective	Liver biopsy (> 5% hepatocyte steatosis)	CNN	50% had steatosis < 30%	N/A	5-fold cross-validation	1	0.882	38	3	15	0
Biswas et al.	Portugal	Retrospective	Liver biopsy (not defined)	CNNa, SVM, ELM	36/63	N/A	10-fold cross-validation	1	1	36	0	27	0
Biswas et al.	Portugal	Retrospective	Liver biopsy (not defined)	CNNa, SVM, ELM	N/A	N/A	10-fold cross-validation	1	1	36	0	27	0
Shi et al.	China	Prospective	MRI (> 5% hepatic fat content)	RT	34/60	N/A	10-fold cross-validation	0.875	0.9286	30	2	24	4
Shi et al.	China	Prospective	MRI (> 5% hepatic fat content)	RT	92% had steatosis < 20%	N/A	10-fold cross-validation	0.875	0.9286	30	2	24	4
Han et al.	US	Prospective	MRI (> 5% hepatic fat content)	CNN	70/102	70/102	Validation cohort	0.97	0.94	68	2	30	2
Han et al.	US	Prospective	MRI (> 5% hepatic fat content)	CNN	Average 11 ± 9%	Average 11 ± 8%	Validation cohort	0.97	0.94	68	2	30	2
Zamanian et al.	Poland	Prospective	Liver biopsy (> 5% hepatocyte steatosis)	CNN + SVM	38/55	N/A	10-fold cross-validation	0.972	1	70	0	74	2
Zamanian et al.	Poland	Prospective	Liver biopsy (> 5% hepatocyte steatosis)	CNN + SVM	50% had steatosis < 30%	N/A	10-fold cross-validation	0.972	1	70	0	74	2
Aim: AI-assisted clinical data sets to diagnose NAFLD					NAFLD/total	NAFLD/total
Ma et al.	China	Prospective	Ultrasonography	BNa, kNN, SVM, LR, NB, RF, BN, AdaBoost, HNB, Bagging, AODE	2522/10,508	N/A	10-fold cross-validation	0.675	0.878	1702	974	7012	820
Islam et al	Taiwan	Retrospective	Ultrasonography	LRa, RF, SVM, ANN	593/994	N/A	10-fold cross-validation	0.741	0.649	439	141	260	154
Wu et al.	Taiwan	Retrospective	Ultrasonography	RFa, LR, ANN, NB	377/577	N/A	10-fold cross-validation	0.872	0.859	329	28	172	48
Atabaki-Pasdar et al.	United Kingdom	Retrospective	MRI (⩾ 5% hepatic fat content)	RF	640/1514	1011/4617	Validation cohort	0.67	0.74	677	838	2668	334
Chen et al	China	Retrospective	Ultrasonography	ANN	Total 10,354	2218/4436	Validation cohort	0.837	0.804	1857	435	1783	361
Liu et al.	China	Retrospective	Ultrasonography	XGBoosta, LR, SVM, SGD, CNN, MLP, LSTM	4018/10,373	1860/4942	Validation cohort	0.611	0.909	1136	280	2802	724
Aim: AI-assisted diagnosis of NASH in patients at-risk for NASH
Gallego-Duran et al	Spain	Prospective	Liver biopsy	LR	NASH/NAFLD	NASH/NAFLD	Validation cohort	0.87	0.6	38	17	26	6
Gallego-Duran et al	Spain	Prospective	Liver biopsy	LR	21/39	44/87	Validation cohort	0.87	0.6	38	17	26	6
Naganawa et al.	Japan	Retrospective	Liver biopsy	LR	Total 53	NASH/non-NASH	Validation cohort	No suspicion of fibrosis: 1.00	No suspicion of fibrosis: 0.92	4	1	11	0
Naganawa et al.	Japan	Retrospective	Liver biopsy	LR	Total 53	28-Jul	Validation cohort	Suspicion of fibrosis: 1.00	Suspicion of fibrosis: 0.31	3	11	5	0
Uehara et al.	Japan	Retrospective	Liver biopsy	Rule extraction algorithm	NASH/non-NASH	NASH/non-NASH	Validation cohort	0.862	0.417	56	7	5	9
Uehara et al.	Japan	Retrospective	Liver biopsy	Rule extraction algorithm	79/23	65/12	Validation cohort	0.862	0.417	56	7	5	9
Garcia-Carretero et al.	Spain	Retrospective	Ultrasonography with LFTs	Lasso regression	NASH/non-NASH	NASH/non-NASH	Validation cohort	0.7	0.79	36	83	314	15
Garcia-Carretero et al.	Spain	Retrospective	Ultrasonography with LFTs	Lasso regression	204/1587	51/397	Validation cohort	0.7	0.79	36	83	314	15
Docherty et al.	USA	Retrospective	Liver biopsy	kNN, RF, XGBoosta	NASH/NAFLD	NASH/NAFLD	Validation cohort	0.81	0.66	146	34	68	34
Docherty et al.	USA	Retrospective	Liver biopsy	kNN, RF, XGBoosta	270/152	180/102	Validation cohort	0.81	0.66	146	34	68	34
AI-assisted diagnosis of liver fibrosis in NAFLD
Pournik et al.	Iran	Retrospective	Liver biopsy	ANN	Cirrhotic/non-cirrhotic	Cirrhotic/non-cirrhotic	Validation cohort	0.657	0.987	44	4	309	23
Pournik et al.	Iran	Retrospective	Liver biopsy	ANN	52/248	15/65	Validation cohort	0.657	0.987	44	4	309	23
Gallego-Duran et al.	Spain	Prospective	Liver biopsy	LR	F0-1/F2-4	F0-1/F2-4	Validation cohort	F2-4 0.77	F2-4 0.80	24	11	45	7
Gallego-Duran et al.	Spain	Prospective	Liver biopsy	LR	20/19	56/31	Validation cohort	F2-4 0.77	F2-4 0.80	24	11	45	7
Shahabi et al.	Iran	Retrospective	Elastography	ANN	F0/F1/F2/F3/F4	15% of data set	Validation cohort	F1 0.993	F1 0.757	-	-	-	-
					415/151/132/23/5	(same proportion)		F2 0.939	F2 0.938
								F3 1.000	F3 0.993
								F4 1.000	F4 1.000
Okanoue et al.	Japan	Retrospective	Liver biopsy and ultrasonography	ANN	Normal/F0/F1/F2/F3/F4	F0/F1/F2/F3-F4	Validation cohort	NAFLD (F0) vs.	NAFLD (F0) vs.	50	7	1	16
					48/106/74/56/65/23	17/18/15/24		NASH (F1-4)	NASH (F1-4)
								0.877	0.941
AI-assisted diagnosis of liver fibrosis in NAFLD
Okanoue et al.	Japan	Retrospective	Liver biopsy	ANN	F0/F1/F2/F3/F4	F0/F1/F2/F3-F4	Validation cohort	F0 vs. F1-4: 0.85	F0 vs. F1-4: 0.867	68	4	26	12
					106/74/56/65/23	30/27/24/29		F0-1 vs. F2-4: 0.755	F0-1 vs. F2-4: 0.877	40	7	50	13
								F0-2 vs. F3-4: 0.828	F0-2 vs. F3-4: 0.877	24	10	71	5
Aim: AI-assisted steatosis quantification of pathological specimen
Vanderbeck et al.	USA	Retrospective	Pathologist	SVM	Macrosteatosis/other features	N/A	10-fold cross-validation	0.98	0.94	1072	48	859	28
Vanderbeck et al.	USA	Retrospective	Pathologist	SVM	1100/859	N/A	10-fold cross-validation	0.98	0.94	1072	48	859	28
Liu et al.	China	Prospective	Pathologist	Linear regression	Steatosis grade 0: 0	Steatosis grade 0: 1	Validation cohort	Steatosis	Steatosis	71	0	1	0
					Grade 1: 77	Grade 1: 41		grade 0 vs. ⩾ 1: 0.99	grade 0 vs. ⩾ 1: 1.00	28	6	36	3
					Grade 2: 45	Grade 2: 22		grade ⩽ 1 vs. ⩾ 2: 0.91	grade ⩽ 1 vs. ⩾ 2: 0.85	6	1	63	3
					Grade 3: 24	Grade 3: 9		grade ⩽ 2 vs. 3: 0.67	grade ⩽ 2 vs. 3: 0.98
Sun et al.	USA	Prospective	Pathologist	CNN	30	66	Validation cohort	⩾ 30% steatosis	⩾ 30% steatosis	15	2	73	6
Sun et al.	USA	Prospective	Pathologist	CNN	30	66	Validation cohort	0.714	0.973	15	2	73	6
Teramoto et al.	Japan	Retrospective	Pathologist	Logistic regression	Matteoni classification46	Matteoni classification	Validation cohort	type 1 vs. NASH: 0.879	type 1 vs. NASH: 1.00	29	0	66	4
					Type 1/type 2/type 3-4 (NASH)	Type 1/type 2/type 3-4 (NASH)		type 2 vs. NASH: 0.909	type 2 vs. NASH: 0.909	30	6	60	3
					33/33/33	33/33/33

ANN artificial neural network, AODE aggregating one-dependence estimators, BN Bayesian network, CNN convolutional neural networks, ELM extreme learning machine, F0-4 METAVIR fibrosis staging, FLD fatty liver disease, HNB hidden naïve Bayes, kNN k-nearest network, LFTs liver function tests, LR logistic regression, LSTM long short-term memory, MLP multilayer perceptron, MRI magnetic resonance imaging, NAFLD non-alcoholic fatty liver disease, NASH non-alcoholic steatohepatitis, NB naïve Bayes, RF random forest, RT regression tree, SGD stochastic gradient descent, SVM support vector machine, XGBoost extreme gradient boosting

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