Artificial intelligence for inflammatory bowel disease dysplasia detection: Current evidence and future directions

Table 1 Key artificial intelligence platforms for inflammatory bowel disease and non-inflammatory bowel disease neoplasia detection

AI platform	Indication	Training dataset	IBD specific training	Sensitivity (%)	Specificity (%)	Population validated
EndoBRAIN-Plus[13]	Colorectal CADe	Approximately 68000 endocytoscopic images	No	98.7	77.1	Japan, Europe
GI genius[14]	Polyp detection	MMX trial videos (150 videos, 338 polyps)	No	82	98	United States
Vinsard IBD-CADe[11]	IBD neoplasia	Approximately 1700 IBD dysplasia images	Yes	95.1	98.8	United States
EfficientNet-B3[9]	IBD neoplasia	862 IBDN images + data augmentation	Yes	72.5	82.9	Japan

Table 1 highlights the key commercial and research artificial intelligence systems listed by indication, data source, whether the model has been specifically trained on inflammatory bowel disease images, and reported performance. Illustrative; heterogeneity limits direct comparisons. AI: Artificial intelligence; IBD: Inflammatory bowel disease; CADe: Computer-aided detection; MMX: Medtronic multi-matrix extraction; IBDN: Inflammatory bowel disease neoplasia.

Table 2 Major studies demonstrating dataset shift, bias, and validation outcomes

Ref.	Study design	Model (training data)	Key result: Sensitivity (%)/specificity (%)	Dataset shift/bias findings	External validation
Guerrero Vinsard et al[11], 2023	Retrospective	Non-IBD CADe, retrained IBD	50/65 (non-IBD trained); 95.1/98.8 (IBD-trained)	Major drop in sensitivity with non-IBD, improved after retraining	Single centre (Mayo, Rochester); United States
Yamamoto et al[9], 2022	Retrospective	IBD-trained CNN (EffNet-B3)	72.5/82.9	Generalizability challenges despite some gains	Multicentre; Japan
Abdelrahim et al[12], 2024	Retrospective + prospective	DL hybrid IBD-centric	Detection: 93.5/80.6 (Retrospective). Characterisation: 87.5/80.6 (prospective)	Prospective real-time IBD-AI validation study; small sample limits generalizability	Multicentre; United Kingdom

Table 2 demonstrates the degree to which landmark studies document the impact of dataset shift and bias on performance; despite some improvement after inflammatory bowel disease-specific retraining, generalizability remains a challenge. AI: Artificial intelligence; IBD: Inflammatory bowel disease; CADe: Computer-aided detection; CNN: Convolutional neural network; DL: Deep learning.

Table 3 Ongoing/planned multicentre artificial intelligence-inflammatory bowel disease validation trials

Trial name	Design	Target condition	Sites	Status
Artificial intelligence in IBD-related dysplasia (AID) study[38]	Prospective	IBD dysplasia detection	Multicentre (Australia)	Recruiting
Artificial intelligence and dysplasia detection in inflammatory bowel disease (EIIDISIA study)[39]	Randomized clinical study	CADe vs Chromoendoscopy in IBD dysplasia detection	Multicentre (Spain)	Recruiting

Table 3 highlights several currently active and planned multicentre validation initiatives, such as artificial intelligence in inflammatory bowel disease-related dysplasia (AID study) artificial intelligence-and dysplasia detection in inflammatory bowel disease (EIIDISIA study), which implement many of the components of the proposed roadmap by recruiting a diverse set of inflammatory bowel disease patients and prospectively comparing the performance of artificial intelligence to that of expert clinicians. IBD: Inflammatory bowel disease; CADe: Computer-aided detection.