Choledochoscopic Examination of a 3-Dimensional Printing Model Using Augmented Reality Techniques: A Preliminary Proof of Concept Study
Bibliographic record
Abstract
BACKGROUND: We applied augmented reality (AR) techniques to flexible choledochoscopy examinations. METHODS: Enhanced computed tomography data of a patient with intrahepatic and extrahepatic biliary duct dilatation were collected to generate a hollow, 3-dimensional (3D) model of the biliary tree by 3D printing. The 3D printed model was placed in an opaque box. An electromagnetic (EM) sensor was internally installed in the choledochoscope instrument channel for tracking its movements through the passages of the 3D printed model, and an AR navigation platform was built using image overlay display. The porta hepatis was used as the reference marker with rigid image registration. The trajectories of the choledochoscope and the EM sensor were observed and recorded using the operator interface of the choledochoscope. RESULTS: Training choledochoscopy was performed on the 3D printed model. The choledochoscope was guided into the left and right hepatic ducts, the right anterior hepatic duct, the bile ducts of segment 8, the hepatic duct in subsegment 8, the right posterior hepatic duct, and the left and the right bile ducts of the caudate lobe. Although stability in tracking was less than ideal, the virtual choledochoscope images and EM sensor tracking were effective for navigation. CONCLUSIONS: AR techniques can be used to assist navigation in choledochoscopy examinations in bile duct models. Further research is needed to determine its benefits in clinical settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".