InterNAV2.0: Minimally Invasive Robot-Assisted Tumour Ablative Therapies
Bibliographic record
Abstract
Canadian Snrgical Technologies & Advanced Robotics has undertaken a major initiative in the field of lung cancer treatment. A platform is being developed for tumour ablative therapies using minimally invasive robotic sys tems. As a proof-of-concept for the platform, a system has been built for Brachytherapy as a treatment for lung cancer. This system uses a navigational software, InterNAVl.0, to consolidate ultrasound imaging and electromagnetic positioning.\nEarly work on InterNAVl.0 looked to develop a research tool to handle imaging information obtained through ultrasound imaging. It was not de signed as a navigation and control environment for clinical use. The objective of the research described in this thesis is to make significant enhancements and add new features to InterNAV1.0 in order to obtain a fairly general navi gation and control environment suitable for use in animal and clinical testing. This thesis discusses the development of InterNAV2.0 and includes a set of feature enhancements and architectural changes to InterNAVl.0 to address several shortcomings and make it applicable for clinical use. After an initial study of InterNAVl.O’s capabilities, several improvements were proposed and implemented. These included enhancements to the navigational model and user interface, integrated robotic controls, predictive neural networks, use of embedded sensors, and integration with dosimetry planning software. All of these functional enhancements are part of InterNAV2.0. Testing shows better results from InterNAV2.0 than InterNAV1.0.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".