Towards the development of a sub 500 micron thermally actuated scanning fiber endoscope for detection of lung cancer
Bibliographic record
Abstract
Medical professionals increasingly rely on endoscopes to carry out many minimallyinvasive procedures on patients in order to safely examine, diagnose and treat a myriad of conditions.However, their distal tip size dictates which passages of the body they can be inserted into and consequently what organs they can access.For inaccessible areas and organs, patients are often subjected to intrusive, risky and uncomfortable procedures; diagnosis of lung cancer is one of these cases.Hence, this study sets out to design an endoscope head that has an outer diameter of less than 500 microns, small enough to be inserted into the lungs.To attain this goal, a novel approach based on resonance thermal excitation of a dual clad single mode optical fiber at a location close to its base is proposed.The previously obtained analytical models for describing the lateral vibratory motion of the fixed-free micro-cantilever are used to validate the corresponding physical prototypes.Parameters such as choice of materials, resonance frequency, bonding methods, shape and dimensions of the actuator bridge, structural rigidity, assembly are considered in the physical design of the device.Lateral free-end deflection of the center fiber is used as a benchmark for evaluating performance.The results show that this novel proposal can be used to satisfy the project requirements.
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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 | 0.000 | 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) | 0.001 | 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 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".