Colometer: A real-time quality feedback system for screening colonoscopy
Bibliographic record
Abstract
AIM: To investigate the performance of a new software-based colonoscopy quality assessment system. METHODS: The software-based system employs a novel image processing algorithm which detects the levels of image clarity, withdrawal velocity, and level of the bowel preparation in a real-time fashion from live video signal. Threshold levels of image blurriness and the withdrawal velocity below which the visualization could be considered adequate have initially been determined arbitrarily by review of sample colonoscopy videos by two experienced endoscopists. Subsequently, an overall colonoscopy quality rating was computed based on the percentage of the withdrawal time with adequate visualization (scored 1-5; 1, when the percentage was 1%-20%; 2, when the percentage was 21%-40%, etc.). In order to test the proposed velocity and blurriness thresholds, screening colonoscopy withdrawal videos from a specialized ambulatory colon cancer screening center were collected, automatically processed and rated. Quality ratings on the withdrawal were compared to the insertion in the same patients. Then, 3 experienced endoscopists reviewed the collected videos in a blinded fashion and rated the overall quality of each withdrawal (scored 1-5; 1, poor; 3, average; 5, excellent) based on 3 major aspects: image quality, colon preparation, and withdrawal velocity. The automated quality ratings were compared to the averaged endoscopist quality ratings using Spearman correlation coefficient. RESULTS: Fourteen screening colonoscopies were assessed. Adenomatous polyps were detected in 4/14 (29%) of the collected colonoscopy video samples. As a proof of concept, the Colometer software rated colonoscope withdrawal as having better visualization than the insertion in the 10 videos which did not have any polyps (average percent time with adequate visualization: 79% ± 5% for withdrawal and 50% ± 14% for insertion, P < 0.01). Withdrawal times during which no polyps were removed ranged from 4-12 min. The median quality rating from the automated system and the reviewers was 3.45 [interquartile range (IQR), 3.1-3.68] and 3.00 (IQR, 2.33-3.67) respectively for all colonoscopy video samples. The automated rating revealed a strong correlation with the reviewer's rating (ρ coefficient= 0.65, P = 0.01). There was good correlation of the automated overall quality rating and the mean endoscopist withdrawal speed rating (Spearman r coefficient= 0.59, P = 0.03). There was no correlation of automated overall quality rating with mean endoscopists image quality rating (Spearman r coefficient= 0.41, P = 0.15). CONCLUSION: The results from a novel automated real-time colonoscopy quality feedback system strongly agreed with the endoscopists' quality assessments. Further study is required to validate this approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".