An Introduction to High Intensity Focused Ultrasound: Systematic Review on Principles, Devices, and Clinical Applications
Bibliographic record
Abstract
Ultrasound can penetrate deep into tissues and interact with human tissue via thermal and mechanical mechanisms. The ability to focus an ultrasound beam and its energy onto millimeter-size targets was a significant milestone in the development of therapeutic applications of focused ultrasound. Focused ultrasound can be used as a non-invasive thermal ablation technique for tumor treatment and is being developed as an option to standard oncologic therapies. High-intensity focused ultrasound has now been used for clinical treatment of a variety of solid malignant tumors, including those in the pancreas, liver, kidney, bone, prostate, and breast, as well as uterine fibroids and soft-tissue sarcomas. Magnetic resonance imaging and Ultrasound imaging can be combined with high intensity focused ultrasound to provide real-time imaging during ablation. Magnetic resonance guided focused ultrasound represents a novel non-invasive method of treatment that may play an important role as an alternative to open neurosurgical procedures for treatment of a number of brain disorders. This paper briefly reviews the underlying principles of HIFU and presents current applications, outcomes, and complications after treatment. Recent applications of Focused ultrasound for tumor treatment, drug delivery, vessel occlusion, histotripsy, movement disorders, and vascular, oncologic, and psychiatric applications are reviewed, along with clinical challenges and potential future clinical applications of HIFU.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".