Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine

Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.

Ambient Light Resistant Shortwave Infrared Fluorescence Imaging for Preclinical Tumor Delineation via the pH Low-Insertion Peptide Conjugated to Indocyanine Green

Shortwave infrared (900-1,700 nm) fluorescence imaging (SWIRFI) has shown significant advantages over visible (400-650 nm) and near-infrared (700-900 nm) fluorescence imaging (reduced autofluorescence, improved contrast, tissue resolution, and depth sensitivity). However, there is a major lag in the clinical translation of preclinical SWIRFI systems and targeted SWIRFI probes. Methods: We preclinically show that the pH low-insertion peptide conjugated to indocyanine green (pHLIP ICG), currently in clinical trials, is an excellent candidate for cancer-targeted SWIRFI. Results: pHLIP ICG SWIRFI achieved picomolar sensitivity (0.4 nM) with binary and unambiguous tumor screening and resection up to 96 h after injection in an orthotopic breast cancer mouse model. SWIRFI tumor screening and resection had ambient light resistance (possible without gating or filtering) with outstanding signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values at exposures from 10 to 0.1 ms. These SNR and CNR values were also found for the extended emission of pHLIP ICG in vivo (>1,100 nm, 300 ms). Conclusion: SWIRFI sensitivity and ambient light resistance enabled continued tracer clearance tracking with unparalleled SNR and CNR values at video rates for tumor delineation (achieving a tumor-to-muscle ratio above 20). In total, we provide a direct precedent for the democratic translation of an ambient light resistant SWIRFI and pHLIP ICG ecosystem, which can instantly improve tumor resection.

Bibliometric analysis of the current status and trends on medical hyperspectral imaging

Hyperspectral imaging (HSI) is a promising technology that can provide valuable support for the advancement of the medical field. Bibliometrics can analyze a vast number of publications on both macroscopic and microscopic levels, providing scholars with essential foundations to shape future directions. The purpose of this study is to comprehensively review the existing literature on medical hyperspectral imaging (MHSI). Based on the Web of Science (WOS) database, this study systematically combs through literature using bibliometric methods and visualization software such as VOSviewer and CiteSpace to draw scientific conclusions. The analysis yielded 2,274 articles from 73 countries/regions, involving 7,401 authors, 2,037 institutions, 1,038 journals/conferences, and a total of 7,522 keywords. The field of MHSI is currently in a positive stage of development and has conducted extensive research worldwide. This research encompasses not only HSI technology but also its application to diverse medical research subjects, such as skin, cancer, tumors, etc., covering a wide range of hardware constructions and software algorithms. In addition to advancements in hardware, the future should focus on the development of algorithm standards for specific medical research targets and cultivate medical professionals of managing vast amounts of technical information.