From tissue to sound: A new paradigm for medical sonic interaction design
Bibliographic record
Abstract
Medical imaging maps tissue characteristics into image intensity values, enhancing human perception. However, comprehending this data, especially in high-stakes scenarios such as surgery, is prone to errors. Additionally, current multimodal methods do not fully leverage this valuable data in their design. We introduce "From Tissue to Sound," a new paradigm for medical sonic interaction design. This paradigm establishes a comprehensive framework for mapping tissue characteristics to auditory displays, providing dynamic and intuitive access to medical images that complement visual data, thereby enhancing multimodal perception. "From Tissue to Sound" provides an advanced and adaptable framework for the interactive sonification of multimodal medical imaging data. This framework employs a physics-based sound model composed of a network of multiple oscillators, whose mechanical properties-such as friction and stiffness-are defined by tissue characteristics extracted from imaging data. This approach enables the representation of anatomical structures and the creation of unique acoustic profiles in response to excitations of the sound model. This method allows users to explore data at a fundamental level, identifying tissue characteristics ranging from rigid to soft, dense to sparse, and structured to scattered. It facilitates intuitive discovery of both general and detailed patterns with minimal preprocessing. Unlike conventional methods that transform low-dimensional data into global sound features through a parametric approach, this method utilizes model-based unsupervised mapping between data and an anatomical sound model, enabling high-dimensional data processing. The versatility of this method is demonstrated through feasibility experiments confirming the generation of perceptually discernible acoustic signals. Furthermore, we present a novel application developed based on this framework for retinal surgery. This new paradigm opens up possibilities for designing multisensory applications for multimodal imaging data. It also facilitates the creation of interactive sonification models with various auditory causality approaches, enhancing both directness and richness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".