Design of a Hand Held Minimally Invasive Lung Tumour Localization Device
Bibliographic record
Abstract
Lung cancer is the leading type of cancer that causes death. If diagnosed, the treatment of choice is surgical resection of the tumour. Traditionally, a surgeon feels for the presence of a tumour in open thoracic surgery. However, a minimally invasive approach is desired. A major problem presented by the minimally invasive approach is the localization of the tumour.\nThis project describes the design, analysis, and experimental validation of a novel minimally invasive instrument for lung tumour localization. The instrument end effector is a two degree of freedom lung tissue palpator. It allows for optimal tissue palpation to increase useful sensor feedback by ensuring sensor contact, and prevents tissue damage by uniformly distributing pressure on the tissue with an upper bound force. Finite element analysis was used extensively to guide the design process. The mechanism is actuated using high strength tungsten cables attached to controlled motors. Heat treatment experiments were undertaken with stainless steel alloy 440C for use in the design, achieving a device factor of safety of 4. This factor of safety is based on a 20 N force on the end effector — the approximate weight of a human lung.\nThe design was prototyped and validation experiments were carried out to assess its articulation and its load carrying capacity. Up to 10 N of force was applied to the prototype. Issues to resolve in the current design include cable extension effects and the existence of joint inflection.\nThe end effector was also designed to allow the inclusion of ultrasound, tactile, and kinaesthetic sensors. It is hypothesized that a plurality of sensors will increase the likelihood of positive tumour localization. These sensors, combined with the presented mechanical design, form the basis for research in robotics-assisted palpation. A proof of concept control system is presented for automated palpation.
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 | 0.001 |
| 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".