Deep learning-based analysis of optical imaging for precision medicine applications in smart healthcare

This research presents an innovative framework that integrates optical imaging with deep learning to analyze the therapeutic impact of Rigolazine and Tanshinone in managing preeclampsia. By integrating fluorescence and hyperspectral imaging, this study identified key biomarkers and quantified molecular changes, particularly in oxidative stress and inflammation, to reveal the mechanisms underlying the therapeutic effects of drugs. The deep learning-based model demonstrates exceptional accuracy in processing multimodal biomedical data, validating the therapeutic efficacy of chasteberry glycosides and tanshinone. Furthermore, the study establishes a scalable methodology that can be extended to evaluate other drugs and diseases. With its ability to deliver high-throughput analysis and precise molecular insights, the proposed framework contributes significantly to the advancement of precision medicine. Additionally, this approach holds great potential for applications in public health monitoring and personalized healthcare within smart city ecosystems. Future research will focus on expanding datasets to include diverse pathological conditions and populations, while enhancing the integration of optical imaging and deep learning to improve scalability and analytical efficiency, paving the way for intelligent healthcare innovations.