An optimized cosine-similarity-loss-function based metric learning neural network enables precise noninvasive diagnosis of bladder cancer through in vitro SERS spectral analysis of clinical urine samples | Synapse
March 3, 2026
An optimized cosine-similarity-loss-function based metric learning neural network enables precise noninvasive diagnosis of bladder cancer through in vitro SERS spectral analysis of clinical urine samples
Key Points
Noninvasive diagnosis of bladder cancer achieves high accuracy with optimized neural network methods.
The model recorded a specificity of 92% and sensitivity of 90% in clinical urine samples.
Analysis utilizes in vitro surface-enhanced Raman scattering (SERS) to evaluate urine samples.
Highlights the potential for early detection strategies, emphasizing the need for broader validation.