AI Article Synopsis

  • - Capsule endoscopy (CE) is a key tool for assessing inflammation in Crohn's disease, traditionally evaluated using scores like Lewis score, CECDAI, and ELIAKIM, but recent AI advancements allow for automated frame selection in CE videos.
  • - In this proof-of-concept study involving 61 patients, researchers developed an AI-based scoring system that correlated well with traditional inflammation scores, showing strong statistical relationships between their automated score and established metrics.
  • - The findings suggest that the AI-generated score is a reliable and objective method for evaluating inflammation in Crohn's disease, laying the groundwork for further advancements in CE technology.

Article Abstract

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  = 0.751,  = 0.707,  = 0.655,  = 0.001). We found a strong correlation between LS and ELIAKIM ( = 0.768,  = 0.001) and a very strong correlation between CECDAI and LS ( = 0.854,  = 0.001) and CECDAI and ELIAKIM scores ( = 0.827,  = 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11135072PMC
http://dx.doi.org/10.1177/17562848241251569DOI Listing

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