Application of artificial intelligence in gastroenterology

Table 3 Summary of clinical studies using artificial intelligence in the upper gastrointestinal field

Ref.	Published year	Aim of study	Design of study	Number of subjects	Type of AI	Endoscopic or ultrasoud modality	Outcomes
Takiyama et al[22]	2018	Recognition of anatomical locations of EGD images	Retrospective	Training set: 27335 images from 1750 patients. Validation set: 17081 images from 435 patients	CNN	White-light endoscopy	AUROCs: 1.00 for the larynx and esophagus, and 0.99 for the stomach and duodenum recognition
van der Sommen et al[23]	2016	Discrimination of early neoplastic lesions in Barrett’s esophagus	Retrospective	100 endoscopic images from 44 patients (leave-one-out cross-validation on a per-patient basis)	SVM	White-light endoscopy	Sensitivity: 83%, specificity: 83% (per-image analysis)
Swager et al[24]	2017	Identification of early Barrett’s esophagus neoplasia on ex vivo volumetric laser endomicroscopy images.	Retrospective	60 volumetric laser endomicroscopy images	Combination of several methods (SVM, discriminant analysis, AdaBoost, random forest, etc)	Ex vivo volumetric laser endomicroscopy	Sensitivity: 90%, specificity: 93%
Kodashima et al[25]	2007	Discrimination between normal and malignant tissue at the cellular level in the esophagus	Prospective ex vivo pilot	10 patients	ImageJ program	Endocytoscopy	Difference in the mean ratio of total nuclei to the entire selected field, 6.4 ± 1.9% in normal tissues and 25.3 ± 3.8% in malignant samples
Shin et al[26]	2015	Diagnosis of esophageal squamous dysplasia	Prospective, multicenter	375 sites from 177 patients (training set: 104 sites, test set: 104 sites, validation set: 167 sites)	Linear discriminant analysis	HRME	Sensitivity: 87%, specificity: 97%
Quang et al[27]	2016	Diagnosis of esophageal squamous cell neoplasia	Retrospective, multicenter	Same data from reference number 26	Linear discriminant analysis	Tablet-interfaced HRME	Sensitivity: 95%, specificity: 91%
Horie et al[28]	2019	Diagnosis of esophageal cancer	Retrospective	Training set: 8428 images from 384 patients. Test set: 1118 images from 97 patients	CNN	White-light endoscopy with NBI	Sensitivity 98%
Huang et al[29]	2004	Diagnosis of H. pylori infection	Prospective	Training set: 30 patients. Test set: 74 patients	Refined feature selection with neural network	White-light endoscopy	Sensitivity: 85.4%, specificity: 90.9%
Shichijo et al[30]	2017	Diagnosis of H. pylori Infection	Retrospective	Training set: CNN1: 32208 images; CNN2: images classified according to 8 different locations in the stomach. Test set: 11481 images from 397 patients	CNN	White-light endoscopy	Accuracy: 87.7%, sensitivity: 88.9%, specificity: 87.4%, diagnostic time: 194 s.
Itoh et al[31]	2018	Diagnosis of H. pylori infection	Prospective	Training set: 149 images (596 images through data augmentation. Test set: 30 images	CNN	White-light endoscopy	AUROC: 0.956, sensitivity: 86.7%, specificity: 86.7%,
Nakashima et al[32]	2018	Diagnosis of H. pylori infection	Prospective pilot	222 patients (training set: 162, test set: 60)	CNN	White-light endoscopy and image-enhanced endoscopy, such as blue laser imaging-bright and linked color imaging	AUROC: 0.96 (blue laser imaging-bright), 0.95 (linked color imaging)
Kubota et al[33]	2012	Diagnosis of depth of invasion in gastric cancer	Retrospective	902 images (10 times cross validation)	“backpropagation” ANN	White-light endoscopy	Accuracy: 77.2%, 49.1%, 51.0%, and 55.3% for T1-4 staging, respectively
Hirasawa et al[34]	2018	Detection of gastric cancers	Retrospective	Training set: 13584 images. Test set: 2296 images.	CNN	White-light endoscopy, chromoendoscopy, NBI	Sensitivity: 92.2%, detection rate with a diameter of 6 mm or more: 98.6%
Zhu et al[35]	2018	Diagnosis of depth of invasion in gastric cancer (mucosa/SM1/deeper than SM1)	Retrospective	Training set: 790 images. Test set: 203 images	CNN	White-light endoscopy	Accuracy: 89.2%, AUROC: 0.94, sensitivity: 74.5%, specificity: 95.6%
Kanesakaet al[36]	2018	Diagnosis of early gastric cancer using magnifying NBI images	Retrospective	Training set: 126 images. Test set: 81 images	SVM	Magnifying NBI	Accuracy: 96.3%, sensitivity: 96.7%, specificity: 95%, PPV: 98.3%,
Gatos et al[37]	2017	Diagnosis of chronic liver disease	Retrospective	126 patients (56 healthy controls, 70 with chronic liver disease	SVM	Ultrasound shear wave elastography imaging with a stiffness value-clustering	AUROC: 0.87, highest accuracy: 87.3%, sensitivity: 93.5%, specificity: 81.2%
Kuppili et al[38]	2017	Detection and characterization of fatty liver	Prospective	63 patients who underwent liver biopsy (10 times cross validation)	Extreme Learning Machine to train single-layer feed-forward neural network	Ultrasound liver images	Accuracy: 96.75%, AUROC: 0.97 (validation performance)
Liu et al[39]	2017	Diagnosis of liver cirrhosis	Retrospective	44 images from controls and 47 images from patients with cirrhosis	SVM	Ultrasound liver capsule images	AUROC: 0.951

AI: Artificial intelligence; EGD: Esophagogastroduodenoscopy; CNN: Convolutional neural network; AUROC: Area under receiver operating characteristic; SVM: Support vector machine; HRME: High-resolution microendoscopy; NBI: Narrow band image; H. pylori: Helicobacter pylori; ANN: Artificial neural network; PPV: Positive predictive value.