Artificial intelligence for discrimination of Crohn's disease and gastrointestinal tuberculosis: A systematic review

Mistakes to avoid in the management of abdominal tuberculosis

INTRODUCTION

The diagnosis and management of abdominal tuberculosis, i.e Gastrointestinal Tuberculosis (GITB) and tuberculous peritonitis (TBP) is challenging. Abdominal tuberculosis, presenting usually with abdominal pain, intestinal obstruction, and constitutional symptoms, is typically a paucibacillary condition. The diagnosis hinges on a correct interpretation of clinical, radiological, histological, biochemical, and microbiological findings as also appropriately assessing response to therapy.

AREAS COVERED

The authors review potential missteps that could occur in managing GITB and TBP sourced from published literature and clinical experience. These include avoiding excess use of tests with limited accuracy, understanding limitations of ascitic adenosine deaminase (ADA) and granulomas, avoiding empirical antitubercular therapy (ATT) where possible but also understanding that microbiological tests may not always be positive, and finally not to bank solely on subjective clinical responses but to use objective markers in assessing response to therapy. In addition, diagnosis of predisposing immunosuppressed states, attention to nutrition, appropriate management of sequelae with endoscopic dilatation/surgery, and early surgery when indicated are some of the additional issues discussed.

EXPERT OPINION

In future, a more secure diagnosis banking on the use of better microbiological tools, multiparameter-based models, artificial intelligence-based approaches, and use of advances in -omics-based approaches can improve diagnosis and avoid some missteps.

Automated Endoscopic Diagnosis in IBD: The Emerging Role of Artificial Intelligence

The emerging role of artificial intelligence (AI) in automated endoscopic diagnosis represents a significant advancement in managing inflammatory bowel disease (IBD). AI technologies are increasingly being applied to endoscopic imaging to enhance the diagnosis, prediction of severity, and progression of IBD and dysplasia-associated colitis surveillance. These AI-assisted endoscopy aim to improve diagnostic accuracy, reduce variability of endoscopy imaging interpretations, and assist clinicians in decision-making processes. By leveraging AI, healthcare providers have the potential to offer more personalized and effective treatments, ultimately improving patient outcomes in IBD care.

Artificial intelligence-aided colonoscopic differential diagnosis between Crohn's disease and gastrointestinal tuberculosis

BACKGROUND AND AIM

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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

Advancements in the use of AI in the diagnosis and management of inflammatory bowel disease

Inflammatory bowel disease (IBD) causes chronic inflammation of the colon and digestive tract, and it can be classified as Crohn's disease (CD) and Ulcerative colitis (UC). IBD is more prevalent in Europe and North America, however, since the beginning of the 21st century it has been increasing in South America, Asia, and Africa, leading to its consideration as a worldwide problem. Optical colonoscopy is one of the crucial tests in diagnosing and assessing the progression and prognosis of IBD, as it allows a real-time optical visualization of the colonic wall and ileum and allows for the collection of tissue samples. The accuracy of colonoscopy procedures depends on the expertise and ability of the endoscopists. Therefore, algorithms based on Deep Learning (DL) and Convolutional Neural Networks (CNN) for colonoscopy images and videos are growing in popularity, especially for the detection and classification of colorectal polyps. The performance of this system is dependent on the quality and quantity of the data used for training. There are several datasets publicly available for endoscopy images and videos, but most of them are solely specialized in polyps. The use of DL algorithms to detect IBD is still in its inception, most studies are based on assessing the severity of UC. As artificial intelligence (AI) grows in popularity there is a growing interest in the use of these algorithms for diagnosing and classifying the IBDs and managing their progression. To tackle this, more annotated colonoscopy images and videos will be required for the training of new and more reliable AI algorithms. This article discusses the current challenges in the early detection of IBD, focusing on the available AI algorithms, and databases, and the challenges ahead to improve the detection rate.

New diagnostic strategies to distinguish Crohn's disease and gastrointestinal tuberculosis

PURPOSE OF REVIEW

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.

RECENT FINDINGS

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.

SUMMARY

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

Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications

Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.

Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review

BACKGROUND AND OBJECTIVES

In spite of rapid growth of artificial intelligence (AI) in digestive endoscopy in lesion detection and characterization, the role of AI in inflammatory bowel disease (IBD) endoscopy is not clearly defined. We aimed at systematically reviewing the role of AI in IBD endoscopy and identifying future research areas.

METHODS

We searched the PubMed and Embase database using keywords ("artificial intelligence" OR "machine learning" OR "computer-aided" OR "convolutional neural network") AND ("inflammatory bowel disease" OR "ulcerative colitis" OR "Crohn's") AND ("endoscopy" or "colonoscopy" or "capsule endoscopy" or "device assisted enteroscopy") between 1975 and September 2023 and identified 62 original articles for detailed review. Review articles, consensus guidelines, case reports/series, editorials, letter to the editor, non-peer-reviewed pre-prints and conference abstracts were excluded. The quality of the included studies was assessed using the MI-CLAIM checklist.

RESULTS

The accuracy of AI models (25 studies) to assess ulcerative colitis (UC) endoscopic activity ranged between 86.54% and 94.5%. AI-assisted capsule endoscopy reading (12 studies) substantially reduced analyzable images and reading time with excellent accuracy (90.5% to 99.9%). AI-assisted analysis of colonoscopic images can help differentiate IBD from non-IBD, UC from non-UC and UC from Crohn's disease (CD) (three studies) with 72.1%, 98.3% and > 90% accuracy, respectively. AI models based on non-invasive clinical and radiologic parameters could predict endoscopic activity (three studies). AI-assisted virtual chromoendoscopy (four studies) could predict histologic remission and long-term outcomes. Computer-assisted detection (CADe) of dysplasia (two studies) is feasible along with AI-based differentiation of high from low-grade IBD neoplasia (79% accuracy). AI is effective in linking electronic medical record data (two studies) with colonoscopic videos to facilitate widespread machine learning.

CONCLUSION

AI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions. Large, multi-center, prospective studies and commercially available IBD-specific endoscopic AI algorithms are warranted.