Application of artificial intelligence in gastroenterology

Table 4 Summary of clinical studies using artificial intelligence in the lower gastrointestinal field

Ref.	Published year	Aim of study	Design of study	Number of subjects	Type of AI	Endoscopic modality	Outcomes
Fernandez-Esparrach et al[40]	2016	Detection of colonic polyps	Retrospective	24 videos containing 31 polyps	Window Median Depth of Valleys Accumulation maps	White-light colonoscopy	Sensitivity: 70.4%. Specificity: 72.4%
Misawa et al[41]	2018	Detection of colonic polyps	Retrospective	546 short videos (training set: 105 polyp-positive videos and 306 polyp-negative videos, test set: 50 polyp-positive videos and 85 polyp-negative videos) from 73 full length videos	CNN	White-light colonoscopy	Accuracy: 76.5%. Sensitivity: 90.0%. Specificity: 63.3%.
Urban et al[42]	2018	Detection of colonic polyps	Retrospective	8641 images with 20 colonoscopy videos	CNN	White-light colonoscopy with NBI	Accuracy: 96.4%. AUROC: 0.991
Klare et al[46]	2019	Detection of colonic polyps	Prospective	55 patients	Automated polyp detection software	White-light colonoscopy	Polyp detection rate: 50.9%. Adenoma detection rate: 29.1%
Wang et al[47]	2018	Detection of colonic polyps	Retrospective	Training set: 5545 images from 1290 patients. Validation set A: 27113 images from 1138 patients. Validation set B: 612 images. Validation set C: 138 video clips from 110 patients. Validation set D: 54 videos from 54 patients	CNN	White-light colonoscopy	Dataset A: AUROC: 0.98 for at least one polyp detection, per-image sensitivity: 94.4%, per-image specificity: 95.2%. Dataset B: per-image sensitivity: 88.2%. Dataset C: per-image sensitivity: 91.6%, per-polyp sensitivity: 100%. Dataset D: per-image specificity: 95.4%
Tischendort et al[48]	2010	Classification of colorectal polyps on the basis of vascularization features.	Prospective pilot	209 polyps from 128 patients	SVM	Magnifying NBI images	Accurate classification rate: 91.9%
Gross et al[49]	2011	Differentiation of small colonic polyps of < 10 mm	Prospective	434 polyps from 214 patients	SVM	Magnifying NBI images	Accuracy: 93.1%. Sensitivity: 95.0%. Specificity: 90.3%.
Takemura et al[50]	2010	Classification of pit patterns	Retrospective	Training set: 72 images. Validation set: 134 images	HuPAS software version 1.3	Magnifying endoscopic images with crystal violet staining	Accuracies of the type I, II, IIIL, and IV pit patterns of colorectal lesions: 100%, 100%, 96.6%, and 96.7%, respectively
Takemura et al[51]	2012	Classification of histology of colorectal tumors	Retrospective	Training set: 1519 images. Validation set: 371 images	HuPAS software version 3.1 using SVM	Magnifying NBI images	Accuracy: 97.8%
Kominami et al[52]	2016	Classification of histology of colorectal polyps	Prospective	Training set: 2247 images from 1262 colorectal lesion. Validation: 118 colorectal lesions	SVM with logistic regression	Magnifying NBI images	Accuracy: 93.2%, Sensitivity: 93.0%, Specificity: 93.3%, PPV: 93%, NPV: 93.3%
Byrne et al[53]	2017	Differentiation of histology of diminutive colorectal polyps	Retrospective	Training set: 223 videos, Validation set: 40 videos. Test set: 125 videos	CNN	NBI video frames	Accuracy: 94%, Sensitivity: 98%, Specificity: 83%
Chen et al[54]	2018	Identification of neoplastic or hyperplastic polyps of < 5 mm	Retrospective	Training set: 2157 images. Test set: 284 images	CNN	Magnifying NBI images	Sensitivity: 96.3%, specificity: 78.1%, PPV: 89.6%, NPV: 91.5%
Komeda et al[55]	2017	Discrimination adenomas from non-adenomatous polyps	Retrospective	1200 images from the endoscopic videos (10 times cross validation)	CNN	White-light colonoscopy with NBI and chromoendoscopy	Accuracy in validation: 75.1%
Mori et al[56]	2015	Discrimination of neoplastic changes in small polyps	Retrospective	Test set: 176 polyps form 152 patients	Multivariate regression analysis	Endocytoscopy	Accuracy: 89.2%, Sensitivity: 92.0%
Mori et al[57]	2016	Development of 2nd generation model, which was mentioned in reference number 56	Retrospective	Test set: 205 small colorectal polyps (≤ 10 mm) from 123 patients	SVM	Endocytoscopy	Accuracy: 89% for both diminutive(< 5 mm) and small (< 10 mm) polyps
Misawa et al[58]	2016	Diagnosis of colorectal lesions using microvascular findings	Retrospective	Training set: 979 images, validation set: 100 images	SVM	Endocytoscopy with NBI	Accuracy: 90%
Mori et al[59]	2018	Diagnosis of neoplastic diminutive polyp	Prospective	466 diminutive polyps from 325 patients	SVM	Endocytoscopy with NBI and stained images	Prediction rate: 98.1%
Takeda et al[60]	2017	Diagnosis of invasive colorectal cancer	Retrospective	Training set: 5543 images from 238 lesions. Test set: 200 images	SVM	Endocytoscopy with NBI and stained images	Accuracy: 94.1% Sensitivity: 89.4%, Specificity: 98.9%, PPV: 98.8%, NPV: 90.1%
Maeda et al[61]	2018	Prediction of persistent histologic inflammation in ulcerative colitis patients	Retrospective	Training set: 12900 images.Test set: 9935 images	SVM	Endocytoscopy with NBI	Accuracy: 91%, Sensitivity: 74%, Specificity: 97%

AI: Artificial intelligence; CNN: Convolutional neural network; NBI: Narrow band image; AUROC: Area under receiver operating characteristic; SVM: Support vector machine; PPV: Positive predictive value; NPV: Negative predictive value.