A 3-D-Printed Tactile Probe Based on Fiber Bragg Grating Sensors for Noninvasive Breast Cancer Identification
Bibliographic record
Abstract
Tissue palpation is one of the most popular techniques to detect tissue abnormalities in clinical scenarios, including breast examination. However, the tactile sensation used to identify tumors by the clinician or a woman during breast palpation makes this procedure subjective. Over the past decades, tactile sensors have been developed o to quantitatively discriminate between cancerous and healthy tissues, but most of these systems still suffer from low force sensitivity, high power consumption, reduced sterilization durability, and electrical noise. This study aims at overcoming these limitations by exploiting the advantages of fiber Bragg grating (FBG) technology combined with the ones of 3D printing to develop an innovative tactile probe for breast cancer identification. FBGs have already been proposed for tissue palpation in minimally invasive surgery via tactile sensing, while their application in superficial palpation is still overlooked. To the best of our knowledge, this is the first work in which the FBG integration into 3D printed structures is proposed for breast superficial palpation. Here, we first focused on the sensing unit design optimization via finite element analysis, fabrication, and metrological characterization. Then, the final prototype of the tactile probe integrating multiple identical sensing units was fabricated, and the results of tests on silicone samples with different hardness and on a phantom mimicking breast tissue with an early-stage tumor were discussed. The promising findings will guide further optimization of the tactile probe design to improve system spatial resolution, reduce its encumbrance and provide feedback to the user for applications on patients.
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 | 0.000 |
| 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".