Overview of Therapeutic Ultrasound Applications and Safety Considerations
Bibliographic record
Abstract
Applications of ultrasound in medicine for therapeutic purposes have been accepted and beneficial uses of ultrasonic biological effects for many years. Low-power ultrasound of about 1 MHz has been widely applied since the 1950s for physical therapy in conditions such as tendinitis and bursitis. In the 1980s, high-pressure-amplitude shock waves came into use for mechanically resolving kidney stones, and "lithotripsy" rapidly replaced surgery as the most frequent treatment choice. The use of ultrasonic energy for therapy continues to expand, and approved applications now include uterine fibroid ablation, cataract removal (phacoemulsification), surgical tissue cutting and hemostasis, transdermal drug delivery, and bone fracture healing, among others. Undesirable bioeffects can occur, including burns from thermal-based therapies and severe hemorrhage from mechanical-based therapies (eg, lithotripsy). In all of these therapeutic applications of ultrasound bioeffects, standardization, ultrasound dosimetry, benefits assurance, and side-effect risk minimization must be carefully considered to ensure an optimal benefit to risk ratio for the patient. Therapeutic ultrasound typically has well-defined benefits and risks and therefore presents a manageable safety problem to the clinician. However, safety information can be scattered, confusing, or subject to commercial conflicts of interest. Of paramount importance for managing this problem is the communication of practical safety information by authoritative groups, such as the American Institute of Ultrasound in Medicine, to the medical ultrasound community. In this overview, the Bioeffects Committee of the American Institute of Ultrasound in Medicine outlines the wide range of therapeutic ultrasound methods, which are in clinical use or under study, and provides general guidance for ensuring therapeutic ultrasound safety.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".