Longitudinal image-based prediction of surgical intervention in infants with hydronephrosis using deep learning: Is a single ultrasound enough?
Bibliographic record
Abstract
The potential of deep learning to predict renal obstruction using kidney ultrasound images has been demonstrated. However, these image-based classifiers have incorporated information using only single-visit ultrasounds. Here, we developed machine learning (ML) models incorporating ultrasounds from multiple clinic visits for hydronephrosis to generate a hydronephrosis severity index score to discriminate patients into high versus low risk for needing pyeloplasty and compare these against models trained with single clinic visit data. We included patients followed for hydronephrosis from three institutions. The outcome of interest was low risk versus high risk of obstructive hydronephrosis requiring pyeloplasty. The model was trained on data from Toronto, ON and validated on an internal holdout set, and tested on an internal prospective set and two external institutions. We developed models trained with single ultrasound (single-visit) and multi-visit models using average prediction, convolutional pooling, long-short term memory and temporal shift models. We compared model performance by area under the receiver-operator-characteristic (AUROC) and area under the precision-recall-curve (AUPRC). A total of 794 patients were included (603 SickKids, 102 Stanford, and 89 CHOP) with a pyeloplasty rate of 12%, 5%, and 67%, respectively. There was no significant difference in developing single-visit US models using the first ultrasound vs. the latest ultrasound. Comparing single-visit vs. multi-visit models, all multi-visit models fail to produce AUROC or AUPRC significantly greater than single-visit models. We developed ML models for hydronephrosis that incorporate multi-visit inference across multiple institutions but did not demonstrate superiority over single-visit inference. These results imply that the single-visit models would be sufficient in aiding accurate risk stratification from single, early ultrasound images.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".