Rapid Prediction of Ablation Zones of Irreversible Electroporation With Electrochemical Impedance Spectroscopy and Artificial Neural Network in a Heterogeneous Model
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we combined electrochemical impedance spectroscopy (EIS) and artificial neural network (ANN) to predict the ablation zone of irreversible electroporation (IRE) in a heterogeneous plant model. The heterogeneous plant model was built by implanting an exotic material with a different electrical conductivity (copper or wood) into potato cubes. For each heterogeneous model, 55 IRE trials were performed with the pulse strength of 300–1300 V and the pulse number of 30, 60, or 90 (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100 \mu \text{s}$ </tex-math></inline-formula> in the pulsewidth and the frequency of 1 Hz) for different positions of exotic implants. The ANN for each model was trained, tested, and validated by a total of 165 experimental data with five inputs (pulse strength, pulse number, implant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-/<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-axis values, and impedance variation parameter) and four outputs (ablation area, major axis length (MaAL), minor axis length (MiAL), and ablation boundary in the first quadrant). Both the experiment and simulation results showed that the two implants with different electrical conductivities could distort the electric field distribution in the plant model. This study concludes that the method combining ANN and EIS can be used to predict the ablation zone of heterogeneous IRE with acceptable accuracy (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$>$ </tex-math></inline-formula>90%), which might bring a hint to the rapid monitoring of IRE in the treatment of tumors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it