A Comprehensive Survey on Deep Learning based Medical Imaging for Chronic Kidney Disease Diagnosis
Bibliographic record
Abstract
Chronic kidney disease is an increasing global health issue that can be better managed with earlier, noninvasive detection. This survey examines contemporary deep-learning methodologies that integrate ultrasound, CT, MRI and retinal imaging with convolutional architectures for the detection, segmentation and prognosis of chronic kidney disease (CKD). In the studies chosen, deep models consistently outperform classical methods in segmentation (with Dice increases of about $\mathbf{0. 1 0 - 0. 1 7}$) and classification (with several studies reporting accuracy rates of over 90%). Moreover, combining methodological trends $(2 \mathrm{D} \rightarrow 3 \mathrm{D}$ CNNs, transfer learning, multimodal fusion), finding major problems (limited multi-institutional datasets, heterogeneity, interpretability) and suggesting ways to make these methods useful in clinical settings, such as using federated learning and explainable AI, have been documented. This survey quantitatively compares the reported metrics and provides a practical guide for future efforts to develop clinically usable CKD imaging systems.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".