Automated Diagnosis of Liver Allograft Fibrosis using Machine Learning Approaches
Bibliographic record
Abstract
Purpose: To determine if machine learning can be leveraged to develop non-invasive diagnostic tools for liver graft fibrosis.Methods: Using 1,804 ultrasound (US) studies from 1,131 patients with a nested 10-fold cross-validation approach, we trained artificial neural network (ANN) and support vector machine models on demographic, clinical, and serum data to predict significant fibrosis. US images was used to train a residual network 18 (ResNet18) model to non-invasively diagnose advanced fibrosis. Results: The ANN model’s performance was superior with the best models’ validation AUCs ranging from 0.74-0.77 and test set AUC range of 0.77-0.81. The ResNet18 model was unable to diagnose advanced graft fibrosis using US images, leading to the training AUCs range from 0.89-0.97, while the validation and testing AUCs were between 0.43-0.63. Conclusion: This study determined machine learning may be leveraged to non-invasively diagnose graft fibrosis using demographic, clinical, and serum data but not with US imaging.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".