Toward a Personalized Optical Digital Twin: AI-Enabled Raman Spectroscopy for Immune Profiling and Translational Clinical Diagnostics

Raman spectroscopy is evolving from a powerful analytical method into a clinically actionable technology capable of supporting personalized and predictive medicine. A central vision driving our work is the concept of a Personalized Optical Digital Twin: a longitudinal, AI-powered model of a patient’s molecular phenotype that enables early detection of pathological deviations from an individual baseline.
A key enabler of this concept is label-free Raman immune cell typing. We present high-throughput Raman platforms for the rapid phenotyping of leukocytes isolated from small volumes of peripheral blood. By capturing subtle molecular fingerprints of neutrophils, lymphocytes, and monocytes, Raman spectroscopy provides access not only to cell identity but also to functional immune states and host-response signatures. Combined with advanced artificial intelligence, these multidimensional datasets allow robust classification of immune activation patterns and open new avenues for monitoring infection, inflammation, and therapy response. Repeated measurements over time establish individualized reference states that form the basis of an optical digital twin.
The approach extends to real-time clinical intervention, where diagnosis and therapy merge. Translational Raman platforms, including regulatory-oriented endoscopic probes for intraoperative oncology and multimodal fiber-based imaging systems, deliver morphochemical information directly in the surgical field. By integrating coherent Raman imaging with complementary nonlinear optical contrasts, these systems capture structural and chemical information at subcellular resolution. Artificial intelligence translates complex optical datasets into clinically interpretable representations, enabling rapid tissue classification and margin assessment. In this setting, optical sensing and AI-driven interpretation form a closed feedback loop that supports emerging “see-and-treat” workflows.
By linking immune profiling from accessible body fluids with in vivo optical diagnostics, longitudinal AI analysis, and complementary physiological data streams from wearable sensors, we outline a framework in which photonic technologies contribute to a scalable infrastructure for personalized medicine. Continuous wearable monitoring provides contextual physiological parameters such as heart rate, activity, and sleep patterns that enrich interpretation of molecular spectroscopic measurements. The optical digital twin does not rely on single snapshot measurements but on high-quality, standardized, and repeated spectroscopic readouts that capture dynamic biological trajectories within this multimodal data ecosystem. This fusion of photonics, wearable sensing, and artificial intelligence transforms rich molecular information into predictive models with potential impact on early disease detection, therapy guidance, and preventive healthcare.
Acknowledgements
Financial support of the EU, the ” Thüringer Ministerium für Bildung, Wissenschaft und Kultur”, the ”Thüringer Aufbaubank”, the Federal Ministry of Research, Technology and Space, Germany, the German Science Foundation, and the Carl-Zeiss Foundation are greatly acknowledged