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©The Author(s) 2019.
World J Gastroenterol. Apr 14, 2019; 25(14): 1666-1683
Published online Apr 14, 2019. doi: 10.3748/wjg.v25.i14.1666
Published online Apr 14, 2019. doi: 10.3748/wjg.v25.i14.1666
Table 1 Artificial intelligence terminology
Artificial intelligence | Machine intelligence that has cognitive functions similar to those of humans such as “learning” and “problem solving.” |
Machine learning | Mathematical algorithms which is automatically built from given data (known as input training data) and predicts or makes decisions in uncertain conditions without being explicitly programmed |
Support vector machines | Discriminative classifier formally defined by an optimizing hyperplane with the largest functional margin |
Artificial neural networks | Multilayered interconnected network which consists of an input, hidden connection (between the input and output layer), and output layer |
Deep learning | Subset of machine learning technique that composed of multiple-layered neural network algorithms |
Convolutional neural networks | Specific class of artificial neural networks that consists of (1) convolutional and pooling layers, which are the two main components to extract distinct features; and (2) fully connected layers to make an overall classification |
Overfitting | Modelling error which occurs when a certain learning model tailors itself too much on the training dataset and predictions are not well generalized to new datasets |
Spectrum bias | Systematic error occurs when the dataset used for model development does not adequately represent or reflect the range of patients who will be applied in clinical practice (target population) |
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% |
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 |
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% |
Table 5 Summary of clinical studies using artificial intelligence in the capsule endoscopy
Ref. | Published year | Aim of study | Design of study | Number of subjects | Type of AI | Outcomes |
Leenhardt et al[62] | 2019 | Detection of gastrointestinal angiectasia | Retrospective | 600 control images and 600 typical angiectasia images (divided equally into training and test datasets) | CNN | Sensitivity: 100%, specificity: 96%, PPV: 96%, NPV: 100%. |
Zhou et al[63] | 2017 | Classification of celiac disease | Retrospective | Training set: 6 celiac disease patients, 5 controls. Test set: additional 5 celiac disease patients, 5 controls | CNN | Sensitivity: 100%, specificity: 100% (for test dataset) |
He et al[64] | 2018 | Detection of intestinal hookworms | Retrospective | 440000 images | CNN | Sensitivity: 84.6%, specificity: 88.6% |
Seguí et al[65] | 2016 | Characterization of small intestinal motility | Retrospective | 120000 images (training set: 100000, test set: 20000) | CNN | Mean classification accuracy: 96% |
- Citation: Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019; 25(14): 1666-1683
- URL: https://www.wjgnet.com/1007-9327/full/v25/i14/1666.htm
- DOI: https://dx.doi.org/10.3748/wjg.v25.i14.1666