Using Deep Learning to Determine Kidney Function Decline in Patients with ADPKD
Bibliographic record
Abstract
Background: Autosomal dominant polycystic kidney disease (ADPKD) is associated with progressive kidney cyst growth, eventually leading to kidney failure in many patients. The current risk stratification tool uses height-adjusted total kidney volume (TKV) by age. However, it does not include clinical or other imaging features that may contribute to kidney function decline. In this study, we developed a deep learning classifier that integrates clinical and imaging features to predict rapid estimated GFR (eGFR) decline in patients with ADPKD. Methods: We included 120 patients with confirmed ADPKD with at least one MRI scan and at least 3 serial eGFR measures. An eGFR decline of 4 mL/min/1.73 m2 per year or greater was defined as rapid progression. We tripled the sample size by extracting the largest manually segmented 2D MRI slice and its two neighbouring slices for each patient and assigning the corresponding clinical features. Pixel-level and spatial-level transformations were applied to the images to enhance our deep model’s generalizability and robustness. The model consisted of three components: 1) EfficientNet-b2, 2) FuseNet, and 3) Classifier with the train:validation:test split of 63:17:20. The EfficientNet-b2 extracted 1000 features from each MRI slice. The FuseNet performed feature fusion on 15 clinically relevant features associated with ADPKD. The feature maps obtained from both models were concatenated and fed into the Classifier. Weighted-average F1 and AUC scores and the confusion matrix were used to assess the model’s performance. Results: The mean age of the study cohort was 46 years (SD 14), 54% were male, 95% were non-Black, hypertension prevalence was 79%, the median eGFR was 67 ml/min/1.73 m2 (IQR 45-99), height-adjusted TKV was 940 mL/m (IQR 542-1373), tolvaptan use was 53%, and the Mayo Imaging Class was 8%, 17%, 40%, 23%, 10%, and 2% for 1A, 1B, 1C, 1D, 1E and class 2, respectively. The weighted-average F1 and AUC scores and the true positive and true negative values were 0.88, 0.88, 0.88, and 0.88 on the validation set, respectively and 0.83, 0.83, 0.86, and 0.80 on the test set. Conclusions: Our study demonstrates that a deep learning approach which integrates clinical information with MRI features can successfully classify patients at risk for rapid eGFR decline. Further validation in an external cohort is required.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".