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

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.