ReviewInflammatory bowel diseases2020
Potential for Standardization and Automation for Pathology and Endoscopy in Inflammatory Bowel Disease.
Review in Inflammatory bowel diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 16 citations in OpenAlex.
- Eleven Grand Challenges for Inflammatory Bowel Disease Genetics and Genomics.Inflammatory bowel diseases · 2025Review
- The impact of artificial intelligence on the endoscopic assessment of inflammatory bowel disease-related neoplasia.Therapeutic advances in gastroenterology · 2025Review
- Histopathology imaging and clinical data including remission status in pediatric inflammatory bowel disease.Scientific data · 2024Article
- Cross-scale multi-instance learning for pathological image diagnosis.Medical image analysis · 2024Article
- Ultrastructural changes in chronic inflammatory enteropathies-a comparison between dogs and humans.Frontiers in cell and developmental biology · 2024Article
- Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images.Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings · 2022Article
- Inflammatory Bowel Disease Management during the COVID-19 Pandemic: A Literature Review.Middle East journal of digestive diseases · 2022Review
- Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer.IEEE journal of translational engineering in health and medicine · 2022Article
- Article
- Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions.World journal of gastroenterology · 2021Review
- Molecular Imaging of Inflammatory Disease.Biomedicines · 2021Review
- Defining the Path Forward for Biomarkers to Address Unmet Needs in Inflammatory Bowel Diseases.Inflammatory bowel diseases · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Automated image analysis methods have shown potential for replicating expert interpretation of histology and endoscopy images, which traditionally require highly specialized and experienced reviewers. Inflammatory bowel disease (IBD) diagnosis, severity assessment, and treatment decision-making require multimodal expert data interpretation and integration, which could be significantly aided by applications of machine learning analyses. This review introduces fundamental concepts of machine learning for imaging analysis and highlights research and development of automated histology and endoscopy interpretation in IBD. Proof-of-concept studies strongly suggest that histologic and endoscopic images can be interpreted with similar accuracy as knowledge experts. Encouraging results support the potential of automating existing disease activity scoring instruments with high reproducibility, speed, and accessibility, therefore improving the standardization of IBD assessment. Though challenges surrounding ground truth definitions, technical barriers, and the need for extensive multicenter evaluation must be resolved before clinical implementation, automated image analysis is likely to both improve access to standardized IBD assessment and advance the fundamental concepts of how disease is measured.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.