Model based deep learning method for focused ultrasound pathway scanning
Bibliographic record
Abstract
The primary purpose of high-intensity focused ultrasound (HIFU), a non-invasive medical therapy, is to precisely target and ablate tumors by focusing high-frequency ultrasound from an external power source. A series of ablations must be performed in order to treat a big volume of tumors, as a single ablation can only remove a small amount of tissue. To maximize therapeutic efficacy while minimizing adverse side effects such as skin burns, preoperative treatment planning is essential in determining the focal site and sonication duration for each ablation. Here, we introduce a machine learning-based approach for designing HIFU treatment plans, which makes use of a map of the material characteristics unique to a patient alongside an accurate thermal simulation. A numerical model was employed to solve the governing equations of HIFU process and to simulate the HIFU absorption mechanism, including ensuing heat transfer process and the temperature rise during the sonication period. To validate the accuracy of this numerical model, a series of tests was conducted using ex vivo bovine liver. The findings indicate that the developed models properly represent the considerable variances observed in tumor geometrical shapes and proficiently generate well-defined closed treated regions based on imaging data. The proposed strategy facilitated the formulation of high-quality treatment plans, with an average tissue over- or under-treatment rate of less than 0.06%. The efficacy of the numerical model in accurately predicting the heating process of HIFU, when combined with machine learning techniques, was validated through quantitative comparison with experimental data. The proposed approach in cooperation with HIFU simulation holds the potential to enhance presurgical HIFU plan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".