A Novel Ureteroscopy Training Platform That Utilizes CT Urograms to Replicate Complex Renal Collecting System Anatomies
Bibliographic record
Abstract
Introduction: Flexible ureteroscopy (fURS) is a one-person surgical technique, limiting trainees' ability to practice intraoperatively. Although well suited for simulation training, few existing fURS simulators can accurately reproduce complex renal collecting system anatomies. We developed an anatomically accurate fURS simulator using three-dimensional (3D) reconstruction of CT urograms and 3D printing technology to address this need. Materials and Methods: Patient-specific CT urograms were used to create 3D reconstruction of the renal collecting system using Slicer™. 3D models were modified using Blender™. Hollow, elastomer kidney models were created using an Objet 3D™ printer. To test and evaluate the new fURS simulator, 25 volunteers were recruited (5 novices, 13 residents, and 7 urologists). Participants were asked to explore the model with fURS and were evaluated on their ability to deduce its 3D anatomy, their ability to navigate to prespecified calices, and their time to task completion. Furthermore, participants were asked to compare the anatomical model with existing fURS benchtop models (Cook Medical™ and Limbs & Things™) on several criteria, including internal visualization, tactile feedback, and overall functional and teaching fidelity, in a survey. Results: We were able to create a fURS simulator that accurately replicates anatomically complex renal collecting systems. In exploring the model, we noted that unlike staff urologists, novices and residents often completely missed lower pole calices. A survey comparison between our simulator and comparable benchtop simulators revealed consistently better ratings of our simulator on all criteria (p < 0.05). Conclusions: We were able to create an anatomically accurate fURS simulator that provides a more realistic scoping experience. Preliminary testing revealed that trainees will benefit from this simulator, particularly with respect to learning how to navigate challenging collecting systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".