Bibliographic record
Abstract
OBJECTIVE: In colonoscopy, it is desirable to accurately localize the position of the endoscope's distal tip. Current tip localization techniques are not sufficient for recording the position and movement of the tip, nor is its rotation measured. We hypothesize that integration of multiple tracking modalities can effectively record the endoscope's motion in real time and continuously corrects cumulative errors. METHODS: A dual modality tracking method is developed to measure the motion of the endoscope's insertion tube in real time, including insertion length, rotation angle, and their velocities. Optical trackballs were used to measure the endoscope insertion tube's motion and cameras were used to correct cumulative errors. RESULTS: The accuracy of insertion length and rotational angle were measured. For speeds ≤ 10 mm/s, the median and 90th percentile insertion position errors were 0.88 mm and 2.2 mm, respectively. The insertion position error increases with the speed, reaching a maximum of 10 mm for speeds < 40 mm/s. 11° and 21° were the median and 90th percentile rotation angle errors for angular speeds < 40°/s. Cumulative errors are sufficiently reduced by the imaging modality. CONCLUSION: The prototype device can precisely measure an unmodified endoscope's position, rotation, and motion in real time without significant accumulative error. The prototype device is small and compatible with existing commercial endoscopes as an add-on accessory, which could be used for reporting, localizing the lesions in follow up procedures, operational guidance, quality assurance, and training. Clinical and Translational Impact Statement-This preclinical research develops an endoscope tracker that can be integrated into colonoscopy training, automatically record endoscope motion, and be further developed to improve polyp and tumor localization during colonoscopy.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".