Flexible and Wearable Ultrasound Device for Medical Applications: A Review on Materials, Structural Designs, and Current Challenges
Bibliographic record
Abstract
Abstract Flexible and miniaturized devices inspired by the advances in electronic materials, fabrication technologies, and wireless communication have emerged as the next‐generation smart devices for medicine and healthcare. One of the most promising applications is the flexible devices and systems for medical ultrasound imaging such as ultrasound transducers. Herein, the recent progress in flexible and wearable devices for medical ultrasound imaging is broadly reviewed, focusing on technologies and potential applications in diagnosis and medical care. First, the progressive prospect of wearable devices is briefed, followed by an introduction of the state‐of‐art advances in material development and fabrication technologies. Second, the emerging technologies of flexible, thin‐film ultrasound transducers is focused in comparison to conventional rigid ultrasound devices. Third, this review highlights recent biomedical applications of the flexible ultrasound transducer. Last but not the least, current challenges and future developments are also discussed from the perspectives of medical ultrasound imaging. The flexible ultrasound transducers with capabilities of mass‐fabrication, versatile integration, and on‐skin conformability can add unprecedented abilities such as medical imaging and diagnosis to the flexible, skin‐wearable devices that are promising to improve the quality of personalized care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".