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Precision screening with sequential multi-algorithm reclassification technique (SMART): Saving bladders from

Sungwook Park¹, Heeseok Kang¹, Yukyoung Choi²

¹Center for Advanced Biomolecular Recognition, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea.

Computers in Biology and Medicine

|March 10, 2025

Summary

A novel double-stage screening system accurately detects bladder cancer using a urinary biosensor and AI. This approach significantly reduces false negatives, improving early cancer diagnosis and patient outcomes.

Keywords:

Artificial intelligence Bladder cancer Cancer screening Explainable artificial intelligence Urine

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Area of Science:

Biomedical Engineering
Oncology
Artificial Intelligence

Background:

Advanced bladder cancer diagnosis necessitates invasive treatments.
Non-invasive biosensors and AI screening show promise but struggle with false negatives.
False negatives in cancer detection can have fatal consequences.

Purpose of the Study:

To develop a double-stage cancer screening system for bladder cancer.
To improve diagnostic accuracy and minimize false negatives using AI and biosensors.
To leverage Explainable AI (XAI) for enhanced model interpretation and improvement.

Main Methods:

A sensitive urinary electrical biosensor measured four biomarkers (CK8, CK18, PD-1, PD-L1).
An initial screening used the CatBoost model with biosensor data, gender, and age.
A second-stage screening employed neural networks with local explanations for reclassification.

Main Results:

The double-stage system successfully reclassified all initial false negatives as cancer patients.
Explainable AI tools provided insights for AI model improvement.
The system demonstrated potential for high accuracy in bladder cancer screening.

Conclusions:

The developed double-stage screening system effectively addresses the critical issue of false negatives in bladder cancer detection.
Integrating biosensors, AI, and XAI offers a powerful non-invasive approach for precision cancer screening.
Further AI model optimization using biomarker feature insights can enhance diagnostic performance.