Accelerating Endoscopic Diagnosis by Videomics
Bibliographic record
Abstract
Videomics, an emerging interdisciplinary field, harnesses the power of artificial intelligence (AI) and machine learning (ML) for the analysis of videoendoscopic frames to improve diagnostic accuracy, therapeutic management, and patient follow-up in medical practice. This article reviews recent advancements and challenges in the application of AI and ML techniques, such as supervised learning, self-supervised learning, and few-shot learning, in videomics for otolaryngology-head-and-neck surgery. We discuss key concepts and tasks in videomics, including quality assessment of endoscopic images, classification of pathologic and nonpathologic frames, detection of lesions within frames, segmentation of pathologic lesions, and in-depth characterization of neoplastic lesions. Furthermore, the potential applications of videomics in surgical training, intraoperative decision-making, and workflow efficiency are highlighted. Challenges faced by researchers in this field, primarily the scarcity of annotated datasets and the need for standardized evaluation methods and datasets, are examined. The article concludes by emphasizing the importance of collaboration among the research community and sustained efforts in refining technology to ensure the successful integration of videomics into clinical practice. The ongoing advancements in videomics hold significant potential in revolutionizing medical diagnostics and treatment, ultimately leading to improved patient outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".