Efficacy Evaluation of SAVE for the Diagnosis of Superficial Neoplastic Lesion
Bibliographic record
Abstract
The detection of non-polypoid superficial neoplastic lesions using current standard of white light endoscopy surveillance and random biopsy is associated with high miss rate. The subtle changes in mucosa caused by the flat and depressed neoplasms often go undetected and do not qualify for further investigation, e.g., biopsy and resection, thus increasing the risk of cancer advancement. This paper presents a screening tool named the saliency-aided visual enhancement (SAVE) method, with an objective of highlighting abnormalities in endoscopic images to detect early lesions. SAVE is a hybrid system combining image enhancement and saliency detection. The method provides both qualitative enhancement and quantitative suspicion index for endoscopic image regions. A study to evaluate the efficacy of SAVE to localize superficial neoplastic lesion was performed. Experimental results for average overlap index >0.7 indicated that SAVE was successful to localize the lesion areas. The area under the receiver-operating characteristic curve obtained for SAVE was 94.91%. A very high sensitivity (100%) was achieved with a moderate specificity (65.45%). Visual inspection showed a comparable performance of SAVE with chromoendoscopy to highlight mucosal irregularities. This paper suggests that SAVE could be a potential screening tool that can substitute the application of burdensome chromoendoscopy technique. SAVE method, as a simple, easy-to-use, highly sensitive, and consistent red flag technology, will be useful for early detection of neoplasm in clinical applications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".