Artificial intelligence in gastroenterology practice: Current perspectives and emerging prospects

Table 3 Studies evaluating applications of artificial intelligence in small intestinal diseases

Ref.	Clinical application	Data type	AI method	Sample size	Study design, center design, and validation design	Major performance metrics	Principal limitation
Aoki et al[47]	Detection of various small intestinal mucosal lesions	CE images for training and videos for testing	Deep CNN	66028 images for training, 379 small-bowel videos for testing	Multicenter retrospective, independent test set of consecutive videos from 3 institutions, external validation	Per-patient detection rate: 99%, sensitivity for mucosal breaks 100% (95%CI: 88.4%-100%) and bleeding 100%, substantially reduced reading workload compared with conventional method	Per-patient assessment may not reflect detection of every individual small lesion
Ding et al[48]	Detection of various small intestinal mucosal lesions	SB-CE images and videos	ResNet-based CNN auxiliary model	Training: 158235 images/1970 patients, validation: 113426569 images/5000 patients	Multicenter retrospective, 77 medical centers, comparison of conventional and AI-assisted reading in 5000 patients, internal validation	Sensitivity: 99.88% (95%CI: 99.67%-99.96%) per patient and 99.90% (95%CI: 99.74%-99.97%) per lesion, reading time reduced from 96.6 ± 22.4 to 5.9 ± 2.1 minutes/patient, diagnostic yield increased from 74.57% to 99.88%	Retrospective design requires prospective evaluation
Liu et al[49]	Small-bowel tumor detection	Clinical patient video data	Multi-scale curvelet analysis and fractal technology with SVM	89 clinical patients	Retrospective, single-center context, feature extraction and classification testing on clinical video frames, internal validation	Sensitivity: 97.8%, specificity: 96.7%	Small dataset of only 89 patients
Leenhardt et al[50]	Detection of gastrointestinal angiectasia	SB-CE still frames	CNN with semantic segmentation	6360 frames from 4166 CE videos, 600-frame independent test set	Multicenter retrospective using a national database, two separate datasets for ML and algorithm testing, internal validation	Sensitivity: 100% (95%CI: 100%-100%), specificity: 96% (95%CI: 93.78%-98.22%)	Focused exclusively on typical GIA, performance on full-length videos not confirmed
Mascarenhas et al[51]	Detection of panendoscopic vascular lesions	Multi-brand and multi-device CE frames	RegNetY-based CNN	34655 frames from 1188 examinations	Multicenter retrospective cohort study, 5-fold cross-validation and independent exam-split test set	Sensitivity: 86.4%, specificity: 98.3%, accuracy: 95.0%, AUC 0.984 (95%CI: 0.977-0.991)	Retrospective selection bias, small number of frames in the testing dataset
Brodersen et al[52]	Detection of suspected CD	Pan-enteric CE videos	AXARO® DL platform	131 patients with suspected CD	Multicenter prospective, blinded trial, prospective external validation	Sensitivity: 92%-96%, specificity: 90%-93%, observer-specific AUC 0.91-0.94 (95%CIs: 0.86-0.98), 97.1% reduction in images requiring review, median reading time approximately 3.2 minutes/patient, 48.5% potentially ruled out without full video review	Relatively small patient cohort
Scheppach et al[54]	Detection of duodenal villous atrophy	Endoscopic still images	ResNet18 DL model	1704 images for internal validation, 349 images for external validation	Multicenter retrospective case-control, internal cross-validation and independent external test set	Sensitivity: 90%, specificity: 76%, accuracy: 84% (external validation)	High risk of selection bias due to retrospective case-control design
Stoleru et al[55]	Detection of celiac disease with villous atrophy	CE images	Linear SVM with image-processing kernels and filters	109 films, 100 frames per film, 51-film test set	Retrospective, single-center, comparison of Linear SVM, Fine KNN, and Weighted KNN, internal validation	Accuracy: 94.1%, sensitivity: 96%	Real-world clinical utility for full video streams was not the primary focus
Gruver et al[57]	Assessment of histologic features of celiac disease	Whole-slide images of IHC sections	DenseNet2 CNN classifiers	116 small-intestine biopsies, matched cohort of 28 patient pairs	Single-center retrospective pilot study, internal validation	More than 90% concordance with manual scores in matched biopsies	Small pilot study, manual selection of tissue by a pathologist remains necessary
Malagelada et al[58]	Identification of intestinal motor dysfunction	CE images	SVM	36 patients and 50 healthy subjects	Single-center prospective pilot study, leave-one-out cross-validation	100% identification of patients meeting manometric criteria for dysmotility	Complexity of parameters analyzed requires a much larger validation dataset
Cheng et al[59]	Detection of high-grade small-bowel obstruction	Abdominal radiographs	Inception-v3 CNN	7768 radiographs from 3212 patients, held-out test set of 2420 consecutive radiographs	Single-center retrospective study, two-stage training and evaluation on a held-out test set, internal validation	Sensitivity: 91.4%, specificity: 91.9%, ROC AUC: 0.971	Retrospective design, potential variation in ground truth as the second test set was assessed by a single radiologist

AI: Artificial intelligence; CE: Capsule endoscopy; CNN: Convolutional neural network; CI: Confidence interval; SB-CE: Small-bowel capsule endoscopy; SVM: Support vector machine; ML: Machine learning; GIA: Gastrointestinal angiectasia; AUC: Area under the curve; CD: Crohn’s disease; DL: Deep learning; KNN: K-nearest neighbors; IHC: Immunohistochemistry; ROC: Receiver operating characteristic; RegNetY: Regular network Y; ResNet: Residual network.