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

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.