Clinical applications of non-invasive multi and hyperspectral imaging of cell and tissue autofluorescence beyond oncology

Label-Free Assessment of Key Biological Autofluorophores: Material Characteristics and Opportunities for Clinical Applications

Autofluorophores are endogenous fluorescent compounds that naturally occur in the intra and extracellular spaces of all tissues and organs. Most have vital biological functions - like the metabolic cofactors NAD(P)H and FAD+, as well as the structural protein collagen. Others are considered to be waste products - like lipofuscin and advanced glycation end products - which accumulate with age and are associated with cellular dysfunction. Due to their natural fluorescence, these materials have great utility for enabling non-invasive, label-free assays with direct ties to biological function. Numerous technologies, with different advantages and drawbacks, are applied to their assessment, including fluorescence lifetime imaging microscopy, hyperspectral microscopy, and flow cytometry. Here, the applications of label-free autofluorophore assessment are reviewed for clinical and health-research applications, with specific attention to biomaterials, disease detection, surgical guidance, treatment monitoring, and tissue assessment - fields that greatly benefit from non-invasive methodologies capable of continuous, in vivo characterization.

Hyperspectral imaging for non-invasive blood oxygen saturation assessment

Hyperspectral Imaging (HSI) seamlessly integrates imaging and spectroscopy, capturing both spatial and spectral data concurrently. With widespread applications in medical diagnostics, HSI serves as a noninvasive tool for gaining insights into tissue characteristics. The distinctive spectral profiles of biological tissues set HSI apart from traditional microscopy in enabling in vivo tissue analysis. Despite its potential, existing HSI techniques face challenges such as alignment issues, low light throughput, and tissue heating due to intense illumination. This study introduces an innovative HSI system featuring active sequential bandpass illumination seamlessly integrated into conventional optical instruments. The primary focus is on analyzing oxyhemoglobin and deoxyhemoglobin saturation in animal tissue samples using multivariate linear regression. This approach holds promise for enhancing noninvasive medical diagnostics. A key feature of the system, active bandpass illumination, effectively prevents tissue overheating, thereby bolstering its suitability for medical applications.

Construction of artificial intelligence non-invasive diagnosis model for common glomerular diseases based on hyperspectral and urine analysis

OBJECTIVE

To develop a non-invasive fluid biopsy assisted diagnosis model for glomerular diseases based on hyperspectral, so as to solve the problem of poor compliance of patients with invasive examination and improve the early diagnosis rate of glomerular diseases.

METHODS

A total of 65 urine samples from patients who underwent renal biopsy from November 2020 to January 2022 in Qianfoshan Hospital of Shandong Province were collected.By simultaneously capturing spectral information of the above urine samples in the 400-1000 nm range, more obvious differences were found in the spectra of urine from patients with glomerular diseases between 650 nm and 680 nm. We obtained the original hyperspectral images in this wavelength range through digital scanning, and sampled pixel points at intervals on the original images. The two-dimensional digital image generated from each pixel point served as a member of the subsequent training and test sets. . After manually labeling the images according to different biopsy pathological types, they were randomly divided into training set (n = 58,800) and test set (n = 25,200). The training set was used for training learning and parameter iteration of artificial intelligence non-invasive liquid diagnosis model, and the test set for model recognition and interpretation. The evaluation indexes such as accuracy, sensitivity and specificity were calculated to evaluate the performance of the diagnosis model.

RESULTS

The model has an accuracy rate of 96% for early diagnosis of four glomerular diseases.

CONCLUSION

The auxiliary diagnosis model system has high accuracy. It is expected to be used as a non-invasive diagnostic method for glomerular diseases in clinic.