Abstract
The objective differentiation of facets of cellular metabolism is important for several clinical applications, including accurate definition of tumour boundaries and targeted wound debridement. To this end, spectral biomarkers to differentiate live and necrotic/apoptotic cells have been defined using in vitro methods. The delineation of different cellular states using spectroscopic methods is difficult due to the complex nature of these biological processes. Sophisticated, objective classification methods will therefore be important for such differentiation. In this study, spectral data from healthy/traumatised cell samples using hyperspectral imaging between 2500–3500 nm were collected using a portable prototype device. Machine learning algorithms, in the form of clustering, have been performed on a variety of pre-processing data types including ‘raw’ unprocessed, smoothed resampling, background subtracted and spectral derivative. The resulting clusters were utilised as a diagnostic tool for the assessment of cellular health and quantified using both sensitivity and specificity to compare the different analysis methods. The raw data exhibited differences for one of the three different trauma types applied, although unable to accurately cluster all the traumatised samples due to signal contamination from the chemical insult. The background subtracted and smoothed data sets reduced the accuracy further, due to the apparent removal of key spectral features which exhibit cellular health. However, the spectral derivative data-types significantly improved the accuracy of clustering compared to other data types, with both sensitivity and specificity for the background subtracted data set being >94% highlighting its utility to account for unknown signal contamination while maintaining important cellular spectral features.
Highlights
Infrared (IR) hyperspectral imaging and spectroscopy methods have been used widely in clinical applications for a variety of medical problems since the 1990s [1]
These methods detect spectral information from the underlying biology and assess differences between healthy and non-healthy tissue and their cellular constituents. Many of such methods are still focused upon the Near Infrared (NIR) optical window, while there has been some insight into the short-wave (SWIR) region, incorporating 900 to 2500 nm [4], where complementary information can assist any assessment or diagnosis
Due to the high absorption of water within the SWIR/MWIR region, biological samples are often chemically ‘fixed’ to remove the unwanted water signature [6], this process can remove significant spectral features for accurate classification [7]. Spectroscopic images of these fixed samples are collected using Atomic Force Microscopy (AFM), requiring the sample to be placed in a vacuum, further reducing the availability of imaging within this region in most studies
Summary
Infrared (IR) hyperspectral imaging and spectroscopy methods have been used widely in clinical applications for a variety of medical problems since the 1990s [1]. One of the most common areas for this technology is within the field of wound healing and diagnostics, covering a range of medical applications including diabetic foot ulcers [2] and burns [3] These methods detect spectral information from the underlying biology and assess differences between healthy and non-healthy tissue and their cellular constituents. Due to the high absorption of water within the SWIR/MWIR region, biological samples are often chemically ‘fixed’ to remove the unwanted water signature [6], this process can remove significant spectral features for accurate classification [7] Spectroscopic images of these fixed samples are collected using Atomic Force Microscopy (AFM), requiring the sample to be placed in a vacuum, further reducing the availability of imaging within this region in most studies. Despite these challenges, imaging further into the IR window could potentially provide complementary information about specific spectral features such as lipids, collagen and other cellular constituents for clinical diagnostics, alongside current imaging modalities including spatial frequency domain imaging (SFDI) [8], laser doppler perfusion imaging (LDPI) [9] and thermography [10], which are more readily available
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