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Introduction: Visual assessment and imaging of the donor liver are inaccurate in predicting fibrosis and remain surrogates for histopathology. We demonstrate that 3‐s scans using a handheld near‐infrared‐spectroscopy (NIRS) instrument can identify and quantify fibrosis in fresh human liver samples. Methods: We undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors. Results: Liver disease patients had a median immature fibrosis of 40% (interquartile range [IQR] 20–60) and mature fibrosis of 30% (10%–50%) on histopathology. The organ donor livers had a median fibrosis (both mature and immature) of 10% (IQR 5%–15%). Using machine learning, this study detected presence of cirrhosis and METAVIR grade of fibrosis with a classification accuracy of 96.3% and 97.2%, precision of 96.3% and 97.0%, recall of 96.3% and 97.2%, specificity of 95.4% and 98.0% and area under receiver operator curve of 0.977 and 0.999, respectively. Using partial‐least square regression machine learning, this study predicted the percentage of both immature (R2 = 0.842) and mature (R2 = 0.837) with a low margin of error (root mean square of error of 9.76% and 7.96%, respectively). Conclusion: This study demonstrates that a point‐of‐care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning. [ABSTRACT FROM AUTHOR]

Abstract Introduction Visual assessment and imaging of the donor liver are inaccurate in predicting fibrosis and remain surrogates for histopathology. We demonstrate that 3‐s scans using a handheld near‐infrared‐spectroscopy (NIRS) instrument can identify and quantify fibrosis in fresh human liver samples. Methods We undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors. Results Liver disease patients had a median immature fibrosis of 40% (interquartile range [IQR] 20–60) and mature fibrosis of 30% (10%–50%) on histopathology. The organ donor livers had a median fibrosis (both mature and immature) of 10% (IQR 5%–15%). Using machine learning, this study detected presence of cirrhosis and METAVIR grade of fibrosis with a classification accuracy of 96.3% and 97.2%, precision of 96.3% and 97.0%, recall of 96.3% and 97.2%, specificity of 95.4% and 98.0% and area under receiver operator curve of 0.977 and 0.999, respectively. Using partial‐least square regression machine learning, this study predicted the percentage of both immature (R2 = 0.842) and mature (R2 = 0.837) with a low margin of error (root mean square of error of 9.76% and 7.96%, respectively). Conclusion This study demonstrates that a point‐of‐care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning.

Aims: Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds. Methods: A hand-held NIR spectrometer was used to scan bone samples from 20 patients and predict: bone volume fraction (BV/TV); and trabecular (Tb) and cortical (Ct) thickness (Th), porosity (Po), and spacing (Sp). Results: NIRS scans on both the inner (trabecular) surface or outer (cortical) surface accurately identified variations in bone collagen, water, mineral, and fat content, which then accurately predicted bone volume fraction (BV/TV, inner R2 = 0.91, outer R2 = 0.83), thickness (Tb.Th, inner R2 = 0.9, outer R2 = 0.79), and cortical thickness (Ct.Th, inner and outer both R2 = 0.90). NIRS scans also had 100% classification accuracy in grading the quartile of bone thickness and quality. Conclusion: We believe this is a fundamental step forward in creating an instrument capable of intraoperative real-time use. Cite this article: Bone Jt Open 2023;4(4):250–261.

Mid-infrared (IR), Raman, and X-ray fluorescence (XRF) spectroscopy methods, as well as mass spectrometry (MS), can be used for 3D chemical imaging. These techniques offer an invaluable opportunity to access chemical features of biological samples in a nonsupervised way. The global chemical information they provide enables the exploitation of a large array of chemical species or parameters, so-called ‘spectromics’. Extracting chemical data from spectra is critical for the high-quality chemical analysis of biosamples. Furthermore, these are the only currently available techniques that can quantitatively analyze tissue content (e.g., molecular concentrations) and substructures (e.g., cells or blood vessels). The development of chemical-derived biological metadata appears to be a new way to exploit spectral information with machine learning algorithms. [ABSTRACT FROM AUTHOR]

Currently, only mass-spectrometry (MS) microscopy brings a quantitative analysis of chemical contents of tissue samples in 3D. Here, the reconstruction of a 3D quantitative chemical images of a biological tissue by FTIR spectro-microscopy is reported. An automated curve-fitting method is developed to extract all intense absorption bands constituting IR spectra. This innovation benefits from three critical features: (1) the correction of raw IR spectra to make them quantitatively comparable; (2) the automated and iterative data treatment allowing to transfer the IR-absorption spectrum into a IR-band spectrum; (3) the reconstruction of an 3D IR-band matrix ( x, y, z for voxel position and a 4th dimension with all IR-band parameters). Spectromics, which is a new method for exploiting spectral data for tissue metadata reconstruction, is proposed to further translate the related chemical information in 3D, as biochemical and anatomical tissue parameters. An example is given with oxidative stress distribution and the reconstruction of blood vessels in tissues. The requirements of IR microscopy instrumentation to propose 3D digital histology as a clinical routine technology is briefly discussed. [ABSTRACT FROM AUTHOR]