An image‐guided high intensity focused ultrasound device for uterine fibroids treatment
Bibliographic record
Abstract
A high intensity focused ultrasound (HIFU) device was developed for treating uterine fibroid tumors. This prototype device enables image-guided therapy by aligning a commercially available abdominal ultrasound image probe to a vaginal HIFU transducer so the HIFU focus is in the image plane. The device was designed based on anatomical constraints of the female pelvic structures. HIFU was generated using a 3.5 MHz PZT-8 crystal, 25.4 mm in diameter, bonded to an aluminum lens. Computer simulations were performed to ensure that effective focusing was achievable at a fixed focal depth of 40 mm. Transducer efficiency was empirically determined to be 58%, and the half pressure maximum focal dimensions were 11 mm in length and 1.2 mm in width. A water-filled latex condom surrounding the transducer provided acoustic coupling, a stand-off, and allowed water circulation for transducer cooling. In vitro experiments in a tissue-mimicking gel phantom and in turkey breast demonstrated ultrasound image-guided lesion formation, or tissue necrosis, at the focus due to HIFU induced thermal and cavitation effects. The HIFU treatment site appeared as a hyperechoic spot on the ultrasound image at intensities above 1250 W/cm2. The results of in vitro experiments and in vivo ergonomic testing in six human volunteers indicated that the device has the potential of providing a nonsurgical approach for uterine fibroid treatment. Future in vivo studies in large animal models and fibroids patients are planned.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".