Enhanced detection of bladder cancer using machine learning analysis of routine urinary sediment data.
Machine learning applied to routine urine samples detects bladder cancer with 84% balanced accuracy, suggesting a practical screening aid.
This algorithm development/validation study examined Enhanced detection of bladder cancer using machine learning analysis of routine urinary sediment data. The PubMed abstract reports that Balanced accuracy was 0;84, and cross-validation demonstrated stable performance (accuracy 0;80).
What the study was
- Study design
- Algorithm development/validation study
- Population
- patients (577 with BC and 764 with benign conditions) were obtained using a fully automated urinary particle
- Sample size
- 1341
- Category
- Early Detection
- Maturity
- Exploratory
- Journal
- Clinical chemistry and laboratory medicine
Why it surfaced
Score 5/10 reflects algorithm development/validation study evidence with abstract only access; genuine match to Early cancer detection.
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