Application of artificial intelligence in gastroenterology

Table 2 Summary of clinical studies using artificial intelligence for recognition of diagnosis and prediction of prognosis

Ref.	Published year	Aim of study	Design of study	Number of subjects	Type of AI	Input variables (number/type)	Outcomes
Pace et al[11]	2005	Diagnosis of gastroesophageal reflux disease	Retrospective	159 patients (10 times cross validation)	“backpropagation” ANN	101/clinical variables	Accuracy: 100%
Lahner et al[12]	2005	Recognition of atrophic corpus gastritis	Retrospective	350 patients (subdivided several times into training and test set equally)	ANN	37 to 3 /clinical and biochemical variables (experiment 1 to 5)	Accuracy: 96.6%, 98.8%, 98.4%, 91.3% and 97.7% (experiment 1-5, respectively)
Pofahl et al[13]	1998	Prediction of length of stay for patients with acute pancreatitis	Retrospective	195 patients (training set: 156, test set: 39)	“backpropagation” ANN	71/clinical variables	Sensitivity: 75 % (for prediction of a length of stay more than 7 d)
Das et al[14]	2003	Prediction of outcomes in acute lower gastrointestinal bleeding	Prospective	190 patients (training set: 120, internal validation set: 70, external validation set: 142)	ANN	26/clinical variables	Accuracy (external validation set): 97% for death, 93% for, recurrent bleeding, 94% for need for intervention
Sato et al[15]	2005	Prediction of 1-year and 5-year survival of esophageal cancer	Retrospective	418 patients (training-: validation-: test set = 53%: 27%: 20%)	ANN	199/ clinicopathologic, biologic, and genetic variables	AUROC for 1 year- and 5 year survival prediction: 0.883 and 0.884, respectively
Rotondano et al[16]	2011	Prediction of mortality in nonvariceal upper gastrointestinal bleeding	Prospective, multicenter	2380 patients (5 × 2 cross-validation)	ANN	68/clinical variables	Accuracy: 96.8%, AUROC: 0.95, sensitivity: 83.8%, specificity: 97.5%,
Takayama et al[17]	2015	Prediction of prognosis in ulcerative colitis after cytoapheresis therapy	Retrospective	90 patients (training set: 54, test set: 36)	ANN	13/clinical variables	Sensitivity: 96.0%, specificity: 97.0%
Hardalaç et al[18]	2015	Prediction of mucosal healing by azathioprine therapy in IBD	Retrospective	129 patients (training set: 103, validation set: 13, test set: 13)	“feed-forward back-propagation” and “cascade-forward” ANN	6/clinical variables	Total correct classification rate: 79.1%
Peng et al[19]	2015	Prediction of frequency of onset, relapse, and severity of IBD	Retrospective	569 UC and 332 CD patients (training set: data from 2003-2010, validation set: data in 2011)	ANN	5/meteorological data	Accuracy in predicting the frequency of relapse of IBD (mean square error = 0.009, mean absolute percentage error = 17.1%)
Ichimasa et al[20]	2018	Prediction of lymph node metastasis, thus minimizing the need for additional surgery in T1 colorectal cancer	Retrospective	690 patients (training set: 590, validation set: 100)	SVM	45/ Clinicopathological variables	Accuracy: 69%, sensitivity: 100%, specificity: 66%
Yang et al[21]	2013	Prediction of postoperative distant metastasis in esophageal squamous cell carcinoma	Retrospective	483 patients (training set: 319, validation set: 164)	SVM	30/7 clinicopathological variables and 23 immunomarkers	Accuracy: 78.7% sensitivity: 56.6%, specificity: 97.7%, PPV: 95.6%, NPV: 72.3%

AI: Artificial intelligence; ANN: Artificial neural network; AUROC: Area under receiver operating characteristic; IBD: Inflammatory bowel disease; UC: Ulcerative colitis; CD: Crohn’s disease; SVM: Support vector machine; PPV: Positive predictive value; NPV: Negative predictive value.