Artificial intelligence in gastroenterology practice: Current perspectives and emerging prospects

Table 4 Studies evaluating applications of artificial intelligence in large intestinal diseases

Ref.	Clinical application	Data type	AI method	Sample size	Study design, center design, and validation design	Major performance metrics	Principal limitation
Misawa et al[60]	Colorectal polyp detection	Colonoscopy videos	Convolutional 3D neural network	73 patients, learning set: 411 short videos/59 patients, independent test set: 135 short videos/14 patients	Single-center retrospective, independent test set, internal validation	Sensitivity 90.0%, specificity 63.3%	High false-positive rate (60%) in polyp-based analysis
Wang et al[63]	Polyp and adenoma detection	Real-time colonoscopy video stream	DL with SegNet-based DCNN	1058 patients, 522 CADe vs 536 control	Single-center RCT	ADR 29.1% vs 20.3% in control, OR 1.61 (95%CI: 1.213-2.135)	Single-center study, inability to blind endoscopists
Gong et al[64]	Adenoma detection and technical quality	Real-time colonoscopy videos	ENDOANGEL with DCNNs and perceptual hash algorithms	704 patients (355 ENDOANGEL vs 349 control)	Single-center RCT	ADR 16% vs 8% in control, OR 2.30 (95%CI: 1.40-3.77)	Single-center study, artificially defined speed categories
Repici et al[61]	Colorectal neoplasia detection	Real-time colonoscopy images	DL with GI-Genius	685 subjects (341 CADe vs 344 control), AI training set: 2684 polyps from 840 patients	Multicenter RCT	ADR 54.8% vs 40.4% in control, RR 1.30 (95%CI: 1.14-1.45)	Expert setting with experienced operators, potential psychological bias
Karsenti et al[62]	Colorectal adenoma detection	Routine real-time colonoscopy	Real-time CADe with GI Genius 2.0.2	2015 participants (1003 CADe vs 1012 control)	Single-center RCT	ADR 37.5% vs 33.7% in control, absolute difference 4.1 percentage points (95%CI: 0.0-8.1, P = 0.051)	Primary ADR outcome reached only borderline statistical significance
Byrne et al[66]	Polyp differentiation, adenoma vs hyperplastic polyp	Unaltered NBI colonoscopy video frames	DCNN with Inception	125 diminutive polyps	Retrospective single-center expert-operator videos, independent test set, internal validation	Accuracy 94% (95%CI: 86-97), sensitivity 98% (95%CI: 92-100) for adenomas	Videos collected by a single expert operator, not tested on live patients
Kather et al[68]	Microsatellite instability prediction	H&E-stained histology slides	Deep residual learning with ResNet18	Various cohorts (discovery: 1380, external: 378)	Multicenter discovery and validation, external validation	Patient-level AUC 0.84 (95%CI: 0.72-0.92) for colorectal cancer	Performance may not generalize beyond cancer types or ethnicities represented in training
Echle et al[69]	Microsatellite instability prediction	Routine H&E histology slides	Modified ShuffleNet DL	8836 colorectal tumors, 6406 development, 771 external resection validation	International multicenter retrospective, external validation	AUROC 0.96 for dMMR	Performance significantly lower in small biopsy samples than resections
Skrede et al[70]	Colorectal cancer prognosis	Scanned conventional H&E sections	Integration of 10 CNNs	2473 training and tuning, 2042 validation data	Retrospective discovery and validation, independent external validation	Hazard ratio 3.84 (95%CI: 2.72-5.43; P < 0.0001) for poor vs good prognosis	Retrospective cohorts, clinical utility requires prospective evaluation
Mossotto et al[71]	Pediatric IBD classification, CD, UC and IBDU	Endoscopic and histological findings from 10 GI locations	Linear SVM with RFE-CV	287 children, 239-patient model development/testing cohort and independent validation of 48 patients	Retrospective single-center, internal and independent validation	Accuracy 82.7% combined model, 83.3% independent validation, IBDU cases were reclassified as CD or UC with > 80% posterior probability	Less accurate for UC (65%) than CD (94%)
Ozawa et al[72]	UC endoscopic activity assessment	Colonoscopy images	CNN with GoogLeNet	26304 images from 841 patients for training, 3981 images from 114 patients for testing	Retrospective image analysis, internal, independent image test set validation	AUROC 0.86 for Mayo 0 and 0.98 for Mayo 0-1	Performance varied by location, lower accuracy in right and left colon than rectum
Maeda et al[73]	UC histologic activity assessment	Endocytoscopy images at 520 × ultra-magnification	SVM using 312 extracted texture features	22835 images from 187 patients, training 12900 images/87 patients, validation 9935 images from 100 patients	Retrospective validation, single-center, temporal training and validation split, internal validation	Accuracy 91% (95%CI: 88-93), sensitivity 74% (95%CI: 65-81), specificity 97% (95%CI: 95-99)	Images collected by experts only, no comparison with long-term clinical prognosis
Gui et al[75]	UC histologic activity assessment	Histological whole-slide images	CNN	138 biopsies from 307 patients	Prospective multicenter, 11 centers, 70% training and 30% testing, internal validation	Accuracy 86%, sensitivity 78%, specificity 91.7%	Lost predictive power in patients who had achieved endoscopic remission
Waljee et al[76]	IBD outcome prediction, UC, CD and indeterminate colitis	Longitudinal laboratory data and demographics from EMR	Random forest	20368 patients	Retrospective cohort, National VHA database, temporal 70% training and 30% testing split, internal validation	AUROC 0.85 (95%CI: 0.84-0.85) to 0.87 (95%CI: 0.87-0.88) depending on model	Older predominantly male population may limit generalizability
Waljee et al[77]	CD outcome prediction	Demographic and laboratory data from clinical trials	Random forest	401 participants, repeated 70/30 training-testing splits	Cohort study of three phase 3 RCTs, repeated 70% and 30% training and testing splits, internal validation	AUROC 0.78 (95%CI: 0.69-0.87) for week-8 model	CRP is an imperfect proxy for remission
Das et al[78]	Acute lower gastrointestinal bleeding risk prediction	Non-endoscopic clinical variables	ANN	332 patients, 120 training, 70 internal validation, 142 external validation	Prospectively collected validation study, multicenter, internal and independent external validation	Accuracy 87%-96% (internal validation), 93%-97% (external validation)	Low PPV for mortality (31%-63%), 26 variables are more cumbersome
Ayaru et al[79]	Acute lower gastrointestinal bleeding risk prediction	Non-endoscopic variables	Gradient boosting	300 patients, 170 for training and internal validation, 130 for external validation	Retrospective database study, multicenter, internal and independent external validation	Accuracy 78%-88% (internal validation), 83%-91% (external validation)	Model requires 39 input variables, mortality was not examined because of its rarity
Klang et al[80]	Acute diverticulitis prediction	EMR data	Gradient boosting	4997 visits, 3154 for internal validation, 1343 for external validation	Multicenter retrospective cohort, internal and held-out external validation	AUC 0.85 (95%CI: 0.78-0.91), sensitivity 88%, NPV 99% (external validation)	Retrospective design and missing data, including CRP missing in 95% of patients
Fukui et al[81]	Irritable bowel syndrome diagnosis	Fecal gut microbiome profiles using 16S rRNA and short-chain fatty acids	Random forest using LASSO feature extraction	111, 85 IBS patients and 26 healthy controls	Prospective diagnostic study, multicenter secondary and tertiary care clinics, internal repeated 10-fold cross-validation	AUC 0.911-0.920, sensitivity > 80%, specificity > 90%	Small control group, lack of dietary information and functional meta-omics investigations

AI: Artificial intelligence; DL: Deep learning; DCNN: Deep convolutional neural network; CADe: Computer-aided detection; RCT: Randomized controlled trial; ADR: Adenoma detection rate; OR: Odds ratio; GI: Gastrointestinal; RR: Relative risk; NBI: Narrow-band imaging; CI: Confidence interval; H&E: Hematoxylin and eosin; AUC: Area under the curve; AUROC: Area under the receiver operating characteristic curve; dMMR: Deficient mismatch repair; CNN: Convolutional neural network; IBD: Inflammatory bowel disease; CD: Crohn’s disease; UC: Ulcerative colitis; IBDU: Inflammatory bowel disease unclassified; SVM: Support vector machine; RFE-CV: Recursive feature elimination with cross-validation; EMR: Electronic medical record; VHA: Veterans Health Administration; CRP: C-reactive protein; ANN: Artificial neural network; PPV: Positive predictive value; NPV: Negative predictive value; IBS: Irritable bowel syndrome; LASSO: Least absolute shrinkage and selection operator; ResNet: Residual network; rRNA: Ribosomal ribonucleic acid; SegNet: Semantic segmentation network.