Disease diagnosis and classification pose significant challenges due to the limited capabilities of traditional methods to obtain molecular information with spatial distribution. Optical imaging techniques, utilizing (auto)fluorescence and nonlinear optical signals, introduce new dimensions for biomarkers exploration that can improve diagnosis and classification. Nevertheless, these signals often cover only a limited number of species, impeding a comprehensive assessment of the tissue microenvironment, which is crucial for effective disease diagnosis and therapy. To address this challenge, we developed a multimodal platform, termed stimulated Raman scattering and second harmonic generation microscopy (SRASH), capable of simultaneously providing both chemical bonds and structural information of tissues. Applying SRASH imaging to azoospermia patient samples, we successfully identified lipids, protein, and collagen contrasts, unveiling molecular and structural signatures for non-obstructive azoospermia. This achievement is facilitated by LiteBlendNet-Dx (LBNet-Dx), our diagnostic algorithm, which achieved an outstanding 100% sample-level accuracy in classifying azoospermia, surpassing conventional imaging modalities. As a label-free technique, SRASH imaging eliminates the requirement for sample pre-treatment, demonstrating great potential for clinical translation and enabling molecular imaging-based diagnosis and therapy.
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