Comprehensive evaluation of a new automatic scoring system for cleanliness assessment in video capsule endoscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A reliable, quick‐to‐assess, and automatic scoring system for cleanliness assessment in video capsule endoscopy (VCE) is presently not available. The present study proposes an approach to automatically assess the cleanliness in VCE frames as per the latest scoring system, that is, Korea‐Canada (KODA). First, a new multi‐label frame dataset containing medical scores of 28 VCE videos was generated through the proposed mobile‐based application called Artificial Intelligence‐KODA (AI‐KODA) score. The scores were saved automatically in real‐time through the application. The generated dataset was transformed into three datasets based on the scores, and each of the dataset was then randomly split into train:validate:test ratio of 60:20:20. Second, a comprehensive evaluation, interpretation, and benchmarking of the three classification tasks were performed with the help of eight transfer learning algorithms on NVIDIA RTX A5000 workstation. Thorough analysis indicates that AI‐KODA utilized with AI is reliable, quick‐to‐access, and free from observer bias. It promotes automatic scoring system for cleanliness assessment in VCE. The meta‐data is available here ( link ).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it