MétaCan
Menu
Back to cohort
Record W88512256 · doi:10.1155/2009/903615

Narrow Band Imaging for the Detection of Neoplastic Lesions of the Colon

2009· review· en· W88512256 on OpenAlexaffvenueabout
Mitchell M Lee, Robert Enns

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2009
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsEndoscopeNarrow-band imagingWhite lightVisualizationBiomedical engineeringMedicineComputer scienceMedical physicsRadiologyArtificial intelligenceComputer visionOpticsEndoscopyPhysics

Abstract

fetched live from OpenAlex

Narrow band imaging (NBI), a technology initially produced by Olympus Medical Systems of Tokyo, Japan, is a relatively new and well-recognized advancement in endoscopic imaging. Other manufacturers have variations of NBI that are similar but not as widely used. The present article will use NBI as a reference point; however, it is recognized that other manufacturers have similar systems with different names that will not be addressed further in the present discussion. The main goal of NBI technology is the ability to predict pathology in real time, based on the mucosal and vascular enhancement offered by endoscopes that have this capability. The purpose of the present paper is to discuss the clinical situations in which NBI technology is used for the detection of neoplastic lesions of the colon as well as the current evidence for or against these practices. Dr Mitchell Lee Although conventional white-light (CWL) imaging uses the entire spectrum of visible light (400 nm to 700 nm), NBI technology is based on the use of optic filters to isolate two specific bands of light: 415 nm (blue) and 540 nm (green) (1–3). By using the different absorptive and reflective properties of these wavelengthts of light on mucosa, an image that enhances the visualization of superficial vascular structures (blue: superficial capillary; green: subepithelial vessels) (1–3) is created. The NBI mode on an endoscope, which can be activated or deactivated by an endoscopist with a control button on the endoscope, typically darkens the appearance of the vessels. Examples of NBI images taken at the St Paul’s Hospital, Vancouver, British Columbia, are shown in Figure 1. Figure 1) Representative photographs of narrow band imaging (NBI) with the endoscope NBI mode deactivated (left panel) and NBI activated (right panel) NBI is often referred to as ‘digital chromoendoscopy’ (4), because it was developed as an alternative method of enhancing the mucosa and vasculature similar to that seen in chromoendoscopy, a technique in which the mucosa is sprayed with a dye (ie, indigo carmine) during the endoscopy procedure. In chromoendoscopy, the absorptive property of the dye, rather than the properties of the light shining onto the surface of the mucosa, is used to enhance the image. The images produced by chromoendoscopy are very similar to the images produced by NBI, with minor differences (5). Although chromoendoscopy is frequently used in Japan, it has not received the same popularity in North America because it is believed by many to be more time consuming to apply the dye, and may require specialized training to perform properly. In the upper gastrointestinal tract, NBI has been used for various disorders such as gastroesophageal reflux disease, Barrett’s esophagitis and gastric neoplasia (3,6–9). In the lower gastrointestinal tract, NBI has been used to detect and assess colon polyps (particularly those that are flat), and for surveillance colonoscopy in patients with ulcerative colitis (UC) and hereditary nonpolyposis colon cancer (HNPCC) (10–13). NBI has also been used in a variety of applications outside of gastroenterology such as laryngoscopy (14) and cystoscopy (15).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.283
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2009
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of GastroenterologySame topicColorectal Cancer Screening and DetectionFrench-language works237,207