Current Status and Emerging Techniques for Measuring the Dielectric Properties of Biological Tissues
Bibliographic record
Abstract
Abstract The dielectric properties of biological tissues are key parameters that support the design and usability of a wide range of electromagnetic-based medical applications, including for diagnostics and therapeutics, and allow the determination of safety and health effects due to exposure to electromagnetic fields. While an extensive body of literature exists that reports on values of these properties for different tissue types under different measurement conditions, it is now evident that there are large uncertainties and inconsistencies between measurement reports. Due to varying measurement techniques, limited measurement validation strategies, and lack of metadata reporting and confounder control, reported dielectric properties suffer from a lack of repeatability and questionable accuracy. Recently, the American Society of Mechanical Engineers (ASME) Thermal Medicine Standards Committee was formed, which included a Tissue Properties working group. This effort aims to support the translation and commercialization of medical technologies, through the development of a standard lexicon and standard measurement protocols. In this work, we present initial results from the Electromagnetic Tissue Properties subgroup. Specifically, this paper reports a critical gap analysis facing the standardization pathway for the dielectric measurement of biological tissues. All established measurement techniques are examined and compared, and emerging ones are assessed. Perspectives on the importance and challenges in measurement validation, accuracy calculation, metadata collection, and reporting are also discussed.
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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 | 0.060 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".