Evolution and New Horizons of Endoscopy in Inflammatory Bowel Diseases

Parigi, Tommaso Lorenzo; Mastrorocco, Elisabetta; Rio, Leonardo Da; Allocca, Mariangela; D’Amico, Ferdinando; Zilli, Alessandra; Fiorino, Gionata; Danese, Silvio; Furfaro, Federica

doi:10.3390/jcm11030872

Cited by 6 publications

(6 citation statements)

References 82 publications

(100 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The introduction of artificial intelligence systems for assisted reading of these exams is expected to address other current limitations of pan-enteric techniques, decreasing reading times and improving lesions’ detection [ 62 ]. In a proof-of-concept study conducted by Ferreira et al [ 63 ] in the setting of CD, a deep learning model for automatic detection of small bowel and colonic ulcers and erosions using PillCam Crohn’s ® capsule images was developed, with an overall sensitivity and specificity for lesions’ detection of 90.0% and 96.0%, respectively.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Pan-Enteric Capsule Endoscopy: Current Applications and Future Perspectives

Rosa,

Andrade,

Lopes

et al. 2023

GE Port J Gastroenterol

View full text Add to dashboard Cite

Background: The role of capsule endoscopy in the evaluation of the small bowel is well established, and current guidelines position it as a first-line test in a variety of clinical scenarios. The advent of double-headed capsules further enabled the endoscopic assessment of colonic mucosa and the opportunity for a one-step noninvasive examination of the entire bowel (pan-enteric capsule endoscopy [PCE]). Summary: We reviewed the technical procedure and preparation of patients for PCE, as well as its current clinical applications and future perspectives. In non-stricturing and non-penetrating Crohn’s disease affecting the small bowel and colon, PCE monitors disease activity by assessing mucosal healing, a major treatment outcome, with a higher diagnostic yield than cross-sectional imaging or conventional colonoscopy. Also in ulcerative colitis, double-headed capsules have been used to monitor disease activity noninvasively. Currently, validated scoring systems have been specifically devised for these double-headed capsules and permit a standardized assessment of the inflammatory burden. In suspected mid-lower digestive bleeding, some exploratory studies have demonstrated the feasibility and high diagnostic yield of PCE, which may work as a filter indicating which patients may benefit of further invasive procedures, namely, for planned hemostatic procedures. The possibility of using PCE is also discussed in the context of polyposis syndromes with simultaneous involvement of the small intestine and colon. Key Messages: PCE is a feasible, effective, and safe diagnostic procedure to evaluate the small bowel and colon. It has been increasingly explored in the setting of inflammatory bowel diseases and, more recently, in suspected mid-lower digestive bleeding. PCE is expected to reduce the demand for invasive procedures and expand the scope of noninvasive intestinal evaluation in the coming future.

show abstract

Section: Clinical Applicationsmentioning

confidence: 99%

Pan-Enteric Capsule Endoscopy: Current Applications and Future Perspectives

Rosa,

Andrade,

Lopes

et al. 2023

GE Port J Gastroenterol

View full text Add to dashboard Cite

show abstract

“… 16 Based on convolutional neural network (CNN) models, AI-powered systems have been developed to decrease reading times and improve lesion detection. 17 To this date, evidence regarding the application of deep learning modules to pan-endoscopy systems is still in its early stages and comes mainly from retrospective studies including a small number of patients and with limited datasets. 18 – 22 …”

Section: Introductionmentioning

confidence: 99%

Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network

Cardoso,

Mascarenhas,

Afonso

et al. 2024

Therap Adv Gastroenterol

View full text Add to dashboard Cite

Background: Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn’s disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn’s Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE. Objectives: In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation. Design: Pan-enteric CE videos (PillCam Crohn’s) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated. Methods: We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS). Results: A total of 61 patients were included. The median LS was 225 (0–6006), CECDAI was 6 (0–33), ELIAKIM was 4 (0–38), and SB_AIS was 0.5659 (0–29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman’s r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM ( r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS ( r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores ( r = 0.827, p = 0.001). Conclusion: Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients.

show abstract

“…Inflammatory bowel disease (IBD) is characterized by chronic intestinal inflammation with periods of remission and relapse [1] and categorized as ulcerative colitis, Crohn's disease, or indeterminate colitis based on a combination of clinical, endoscopic, and histologic data [1,2]. Currently, the gold standard for IBD diagnosis is ileocolonoscopy and esophagogastroduodenoscopy (EGD) combined with histology analysis [3]; however, novel machine and deep learning technologies present promising new avenues to significantly enhance our understanding of IBD and optimize remission standards to aim for better patient outcomes. Additional diagnostic modalities, including capsule endoscopy, magnetic resonance enterography, and computed tomography enterography are becoming more common in aiding diagnosis and evaluation [4,5], and the data produced from these techniques can be analyzed by machine and deep learning models to inform management of disease (Fig.…”

Section: Introductionmentioning

confidence: 99%

Machine and deep learning in inflammatory bowel disease

Zulqarnain¹,

Rhoads²,

Syed³

2023

Current Opinion in Gastroenterology

View full text Add to dashboard Cite

Purpose of reviewThe Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning presents another watershed moment in IBD treatment. Interest in these methods in IBD research has increased over the past 10 years, and they offer a promising path to better clinical outcomes for IBD patients.Recent findingsDeveloping new tools to evaluate IBD and inform clinical management is challenging because of the expansive volume of data and requisite manual interpretation of data. Recently, machine and deep learning models have been used to streamline diagnosis and evaluation of IBD by automating review of data from several diagnostic modalities with high accuracy. These methods decrease the amount of time that clinicians spend manually reviewing data to formulate an assessment.SummaryInterest in machine and deep learning is increasing in medicine, and these methods are poised to revolutionize the way that we treat IBD. Here, we highlight the recent advances in using these technologies to evaluate IBD and discuss the ways that they can be leveraged to improve clinical outcomes.

show abstract

Evolution and New Horizons of Endoscopy in Inflammatory Bowel Diseases

Cited by 6 publications

References 82 publications

Pan-Enteric Capsule Endoscopy: Current Applications and Future Perspectives

Pan-Enteric Capsule Endoscopy: Current Applications and Future Perspectives

Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network

Machine and deep learning in inflammatory bowel disease

Contact Info

Product

Resources

About