Targeting feasibility evaluation of magnetic resonance-guided focused ultrasound in the management of osteomyelitis: a virtual treatment planning study in 75 patients
Bibliographic record
Abstract
Purpose: Osteomyelitis is one of the most serious complications linked to diabetes and increases the possibility of limb amputation considerably. There exists an important clinical need to improve management of osteomyelitis, especially for diabetic patients who are more susceptible to failures, relapses and chronicity of multiple bone infections. Magnetic resonance-guided focused ultrasound (MRgFUS) can offer a clinical management option for patients with osteomyelitis by providing a non-surgical and potentially rapid-recovery treatment option.Material and Methods: A retrospective study with patients with confirmed osteomyelitis (n = 75) was performed at evaluating the feasibility to target bone infection sites with a clinically approved MRgFUS device (Sonalleve, Profound Medical, Mississauga, ON, Canada). The developed methodology allows using preexisting diagnostic magnetic resonance imaging (MRI) or computed tomography (CT) scans to evaluate the treatment feasibility directly using a MRgFUS treatment planning software.Results: 74.7% of the cases included in our study passed the targetability criteria. Cases were deemed non-targetable if the target was less than 1 cm from the skin or close to a neuro-vascular bundle, metallic implants, or in the way of a defect in the overlying skin. For cases that passed the targetability criteria, an average among patients of 92.7 ± 5.2% of the gross treatment volume could be reached using treatment cells available at the Sonalleve system.Conclusion: The retrospective study presented here is the first step to demonstrate the feasibility of utilizing MRgFUS for the thermal treatment of osteomyelitis.
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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) | 0.000 | 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".