Differentiating between Crohn's disease (CD) and gastrointestinal tuberculosis (GITB) is challenging. We aimed to evaluate the clinical applicability of an artificial intelligence (AI) model for this purpose.

The AI model was developed and assessed using an internal dataset comprising 1,132 colonoscopy images of CD and 1,045 colonoscopy images of GITB at a tertiary referral center. Its stand-alone performance was further evaluated in an external dataset comprising 67 colonoscopy images of 17 CD patients and 63 colonoscopy images of 14 GITB patients from other institutions. Additionally, a crossover trial involving three expert endoscopists and three trainee endoscopists compared AI-assisted and unassisted human interpretations.

In the internal dataset, the sensitivity, specificity, and accuracy of the AI model in distinguishing between CD and GITB were 95.3%, 100.0%, and 97.7%, respectively, with an area under the ROC curve of 0.997. In the external dataset, the AI model exhibited a sensitivity, specificity, and accuracy of 77.8%, 85.1%, and 81.5%, respectively, with an area under the ROC curve of 0.877. In the human endoscopist trial, AI assistance increased the pooled accuracy of the six endoscopists from 86.2% to 88.8% (P = 0.010). While AI did not significantly enhance diagnostic accuracy for the experts (96.7% with AI vs 95.6% without, P = 0.360), it significantly improved accuracy for the trainees (81.0% vs 76.7%, P = 0.002).

This AI model shows potential in aiding the accurate differential diagnosis between CD and GITB, particularly benefiting less experienced endoscopists.

This editorial offers an updated synthesis of the major advancements in the management and treatment of inflammatory bowel disease (IBD), as documented in the World Journal of Gastroenterology between 2023 and early 2024. This editorial explores substantial developments across key research areas, such as intestinal microecology, computational drug discovery, dual biologic therapy, telemedicine, and the integration of lifestyle changes into patient care. Furthermore, the discussion of emerging topics, including bowel preparation in colonoscopy, the impact of the coronavirus disease 2019 pandemic, and the intersection between IBD and mental health, reflects a shift toward a more holistic approach to IBD research. By integrating these diverse areas of research, this editorial seeks to promote a holistic and multidisciplinary approach to IBD treatment, combining emerging technologies, personalized medicine, and conventional therapies to improve patient outcomes.

Despite advances in our radiological, histological and microbiological armamentarium, distinguishing between Crohn's disease (CD) and intestinal tuberculosis (ITB), especially in a TB endemic country, continues to be a challenging exercise in a significant number of patients. This review aims to summarize current available evidence on novel diagnostic techniques which have a potential to fill the gap in our knowledge of differentiating between ITB and CD.

Both ITB and CD are associated with altered host immune responses, and detection of these altered innate and adaptive immune cells has potential to distinguish ITB from CD. ITB and CD have different epigenetic, proteomic and metabolomic signatures, and recent research has focused on detecting these differences. In addition, the gut microbiome, which is involved in mucosal immunity and inflammatory responses, is considerably altered in both ITB and CD, and is another potential frontier, which can be tapped to discriminate between the two diseases. With technological advancements, we have newer radiological modalities including perfusion CT and dual-layer spectral detector CT enterography and evidence is emerging of their role in differentiating ITB from CD. Finally, time will tell whether the advent of artificial intelligence, with rapidly accumulating data in this field, will be the gamechanger in solving this puzzle of diagnostic dilemma between ITB and Crohn's disease.

Recent advances need to be clinically validated before they can be used as novel diagnostic measures to differentiate Intestinal TB from CD.