ReviewDEN open2026
Advancements and limitations of image-enhanced endoscopy in colorectal lesion diagnosis and treatment selection: A narrative review.
Review in DEN open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related mortality, highlighting the need for early detection and accurate lesion characterization. Traditional white-light imaging has limitations in detecting lesions, particularly those with flat morphology or minimal color contrast with the surrounding mucosa. It also struggles to distinguish neoplastic from non-neoplastic lesions. These limitations led to the development of image-enhanced endoscopy (IEE). Image-enhanced endoscopy modalities such as narrow-band imaging, blue laser imaging, linked color imaging, and texture and color enhancement imaging enhance mucosal surface and vascular pattern visualization, thereby improving lesion detection and characterization. In contrast, red dichromatic imaging is primarily designed to enhance the visibility of deep blood vessels, making it particularly useful during therapeutic endoscopies, such as identifying bleeding sources and monitoring post-treatment hemostasis. Although IEE enhances lesion detection and characterization, it remains limited in assessing submucosal invasion depth, which is a key factor in treatment decisions. Endoscopic submucosal dissection requires accurate prediction of invasion depth; however, IEE mainly reflects superficial features. Endoscopic ultrasound and artificial intelligence-assisted diagnostics have emerged as complementary techniques for improving depth assessment and lesion classification. Additionally, IEE plays a critical role in detecting ulcerative colitis-associated neoplasia (UCAN), which often presents with a flat morphology and indistinct borders. High-definition chromoendoscopy and IEE modalities enhance detection; however, inflammation-related changes limit diagnostic accuracy. Artificial intelligence and molecular biomarkers may improve UCAN diagnosis. This review examines the role of IEE in lesion detection and treatment selection, its limitations, and complementary techniques such as endoscopic ultrasound and artificial intelligence. We also explored pit pattern diagnosis using crystal violet staining and discussed emerging strategies to refine colorectal cancer screening and management.
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.