Kidney stone compositional analysis and identification using a benchtop high-resolution multi-modal x-ray phase-contrast micro-CT imaging system
Bibliographic record
Abstract
The prevalence of kidney stones has significantly risen among the elderly population in recent decades, with some countries experiencing rates approaching 15%. Due to high recurrence rates, analyzing stone composition is essential for recurrence prevention. Fourier transform infrared spectroscopy (FT-IR) and conventional x-ray diffractometry (XRD) are used for this purpose, however, both methods are labor-intensive and require skilled operators, as stones need careful dissection and grinding. Many kidney stones have heterogeneous compositions, and even with spectroscopic analyses, error rates remain high. Despite improvements, challenges in stone sampling and tiny material amounts can still lead to inaccuracies. This prompts the question of how to better manage patients and potentially enhance recurrence prevention strategies despite the shift toward spectroscopic techniques in analytical labs. In this work, we demonstrate the potential of an in-house grating-based high-resolution x-ray phase-contrast μ-CT imaging system in characterizing kidney stones and their compositional analysis. Employing the reported compact benchtop x-ray phase-contrast μ-CT imaging system facilitates stone identification and analysis using additional quantitative data—phase and scattering—obtained from the emerging x-ray phase-contrast imaging system. Having the three sets of images—transmission, phase, and scattering—could potentially pave the way for an accurate and faster stone compositional analysis with clinical values in studying the pathophysiological mechanisms of kidney stone disease that could help improve the recurrence prevention rates.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".