Multi-model deep learning on photoacoustic flow cytometry signals for real-time melanoma circulating tumor cells detection and biological characterization

• Random Forest achieved AUC 0.97 for melanoma detection using 1064 nm features. • Weakly supervised denoising cut false positives by 42%, raising accuracy to 94%. • State-space PAFCMamba outperformed Transformer, with AUC 0.785 versus 0.730. • Hybrid CNN-Transformer predicted staging & metastasis with AUC up to 0.974. • PAFC spectra linked to immune genes, enabling noninvasive patient stratification. Melanoma remains one of the most aggressive forms of skin cancer, with early detection being critical for patient outcomes. This study introduces a novel photoacoustic fingerprinting approach integrated with advanced machine learning for non-invasive melanoma detection and circulating tumor cell (CTC) identification. We developed a three-tiered photoacoustic fingerprinting system combining photoacoustic flow cytometry (PAFC) with machine learning algorithms. A uniform PAFC configuration employed a 532 nm laser for vascular localization followed by a 1064 nm laser targeting melanin-rich melanoma cells. The study included 50 melanoma patients and healthy controls, analyzing spectral features across multiple wavelengths. We compared self-supervised learning architectures (PAFCMamba vs. Transformer) and developed a hybrid CNN-Transformer model for simultaneous CTC identification, staging, and metastatic dissemination prediction. The photoacoustic fingerprinting system achieved exceptional diagnostic discrimination between melanoma patients and healthy controls. Random Forest achieved area under curve (AUC) values up to 0.97. The PAFCMamba model outperformed the Transformer architecture (accuracy 0.75 vs. 0.62, AUC 0.785 vs. 0.730). The hybrid CNN-Transformer architecture achieved exceptional performance with AUCs up to 0.974 and precision > 94 % in simultaneous CTC detection, staging, and metastasis prediction. High-immunogenicity genes including MLANA, GPR89B/A, and PIGF were identified as potential immunotherapy targets, with photoacoustic signatures serving as non-invasive surrogate biomarkers for underlying molecular characteristics. This study establishes photoacoustic fingerprinting as a clinically viable, non-invasive approach for melanoma detection and CTC monitoring, achieving performance comparable to conventional methods. The integration of machine learning with photoacoustic biomarkers provides a scalable framework with interpretable features that facilitates clinical translation.