Automated Evaluation of Urodynamic Examinations Through Local Linear Models: Validation on Spinal Cord Injury Individuals
Bibliographic record
Abstract
OBJECTIVE: Investigating consistent methods and metrics for classifying Detrusor Overactivity (DO) events and developing an automated robust method for clinical measurements calculation from cystometry data in persons with spinal cord injury (SCI). METHODS AND PROCEDURES: A two-stage method for was proposed to detect DO events. In the first stage, DO peaks were detected using local linear models combined with thresholding criteria derived from clinical definitions and known artifacts. In the second stage, a segmentation method was proposed to detect the start and end time points of each DO event, marking the DO activity periods. As a result, complete clinical measurements, including bladder compliance, can be estimated automatically. The method was developed and tested on 77 anonymized urodynamic samples from SCI individuals (40 DO-positive, 37 DO-negative) with 158 annotated DO events. RESULTS: On test data, in terms of the patient-level diagnosis of DO, the proposed method achieved an accuracy of 100%. Individual DO event detection achieved an average precision of 0.94 and recall of 0.72. Detrusor activity period identification showed a precision of 0.86 and a recall of 0.88. The task of automated bladder compliance estimation showed that the point-value-based method yields a lower median absolute error (MAE) compared to the proposed line-fitting-based method, with a MAE of 5.20 and 7.14 ml/cmH2O, respectively. Finally, for classifying bladder function into normal, low and severely low compliance, the proposed method had an accuracy of 88%. CONCLUSION: This work proposes a fully automated detrusor overactivity diagnosis and feature extraction method. Empowering medical teams to consistently assess urodynamic studies while aiding disease characterization and enhancing clinical decision-making for SCI patients. Furthermore, it provides a mathematically defined method for extending the pipeline to other populations and standardizing clinical assessments.Category: Clinical Engineering, Medical Devices and 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".