Cystoscopic imaging with machine learning algorithm for automated detection of bladder cancer

Stanford researchers at the Liao and Xing Labs have developed and tested a machine learning algorithm for augmented detection of bladder cancer. Machine learning has the potential to enhance medical decision making in cancer detection and image analysis.
This invention enables real time detection of bladder cancer and other tumors in a patient's bladder during the clinic cystoscopy procedure and transurethral resection in the operating room. The machine learning algorithm can be integrated into existing cystoscopy platforms. In comparison with other approved enhanced cystoscopy technologies such as blue light cystoscopy and narrow band imaging, our approach improves cancer detection and without the need for additional cystoscopy equipment or imaging agents.

Stage of Research: