Point-of-care detection of fibrosis in liver transplant surgery using near-infrared spectroscopy and machine learning.

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 (R 2 = 0.842) and mature (R 2 = 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.

Towards a point-of-care multimodal spectroscopy instrument for the evaluation of human cardiac tissue.

To demonstrate that point-of-care multimodal spectroscopy using Near-Infrared (NIR) and Raman Spectroscopy (RS) can be used to diagnose human heart tissue. We generated 105 spectroscopic scans, which comprised 4 NIR and 3 RS scans per sample to generate a "multimodal spectroscopic scan" (MSS) for each heart, done across 15 patients, 5 each from the dilated cardiomyopathy (DCM), Ischaemic Heart Disease (IHD) and Normal pathologies. Each of the MSS scans was undertaken in 3 s. Data were entered into machine learning (ML) algorithms to assess accuracy of MSS in diagnosing tissue type. The median age was 50 years (IQR 49-52) for IHD, 47 (IQR 45-50) for DCM and 36 (IQR 33-52) for healthy patients (p = 0.35), 60% of which were male. MSS identified key differences in IHD, DCM and normal heart samples in regions typically associated with fibrosis and collagen (NIR wavenumbers: 1433, 1509, 1581, 1689 and 1725 nm; RS wavelengths: 1658, 1450 and 1330 cm-1). In principal component (PC) analyses, these differences explained 99.2% of the variation in 4 PCs for NIR, 81.6% in 10 PCs for Raman, and 99.0% in 26 PCs for multimodal spectroscopic signatures. Using a stack machine learning algorithm with combined NIR and Raman data, our model had a precision of 96.9%, recall of 96.6%, specificity of 98.2% and Area Under Curve (AUC) of 0.989 (Table 1). NIR and Raman modalities alone had similar levels of precision at 94.4% and 89.8% respectively (Table 1). MSS combined with ML showed accuracy of 90% for detecting dilated cardiomyopathy, 100% for ischaemic heart disease and 100% for diagnosing healthy tissue. Multimodal spectroscopic signatures, based on NIR and Raman spectroscopy, could provide cardiac tissue scans in 3-s to aid accurate diagnoses of fibrosis in IHD, DCM and normal hearts. Table 1 Machine learning performance metrics for validation data sets of (a) Near-Infrared (NIR), (b) Raman and (c and d) multimodal data using logistic regression (LR), stochastic gradient descent (SGD) and support vector machines (SVM), with combined "stack" (LR + SGD + SVM) AUC Precision Recall Specificity (a) NIR model  Logistic regression 0.980 0.944 0.933 0.967  SGD 0.550 0.281 0.400 0.700  SVM 0.840 0.806 0.800 0.900  Stack 0.933 0.794 0.800 0.900 (b) Raman model  Logistic regression 0.985 0.940 0.929 0.960  SGD 0.892 0.869 0.857 0.932  SVM 0.992 0.940 0.929 0.960  Stack 0.954 0.869 0.857 0.932 (c) MSS: multimodal (NIR + Raman) to detect DCM vs. IHD vs. normal patients  Logistic regression 0.975 0.841 0.828 0.917  SGD 0.847 0.803 0.793 0.899  SVM 0.971 0.853 0.828 0.917  Stack 0.961 0.853 0.828 0.917 (d) MSS: multimodal (NIR + Raman) to detect pathological vs. normal patients  Logistic regression 0.961 0.969 0.966 0.984  SGD 0.944 0.967 0.966 0.923  SVM 1.000 1.000 1.000 1.000  Stack 1.000 0.944 0.931 0.969 Bold values indicate values obtained from the stack algorithm and used for analyses.

Near-infrared spectroscopy for structural bone assessment.

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. 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). 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. We believe this is a fundamental step forward in creating an instrument capable of intraoperative real-time use.

Synchrotron-Infrared Microspectroscopy of Live Leishmania major Infected Macrophages and Isolated Promastigotes and Amastigotes.

The prevalence of neglected tropical diseases (NTDs) is advancing at an alarming rate. The NTD leishmaniasis is now endemic in over 90 tropical and sub-tropical low socioeconomic countries. Current diagnosis for this disease involves serological assessment of infected tissue by either light microscopy, antibody tests, or culturing with in vitro or in vivo animal inoculation. Furthermore, co-infection by other pathogens can make it difficult to accurately determine Leishmania infection with light microscopy. Herein, for the first time, we demonstrate the potential of combining synchrotron Fourier-transform infrared (FTIR) microspectroscopy with powerful discrimination tools, such as partial least squares-discriminant analysis (PLS-DA), support vector machine-discriminant analysis (SVM-DA), and k-nearest neighbors (KNN), to characterize the parasitic forms of Leishmania major both isolated and within infected macrophages. For measurements performed on functional infected and uninfected macrophages in physiological solutions, the sensitivities from PLS-DA, SVM-DA, and KNN classification methods were found to be 0.923, 0.981, and 0.989, while the specificities were 0.897, 1.00, and 0.975, respectively. Cross-validated PLS-DA models on live amastigotes and promastigotes showed a sensitivity and specificity of 0.98 in the lipid region, while a specificity and sensitivity of 1.00 was achieved in the fingerprint region. The study demonstrates the potential of the FTIR technique to identify unique diagnostic bands and utilize them to generate machine learning models to predict Leishmania infection. For the first time, we examine the potential of infrared spectroscopy to study the molecular structure of parasitic forms in their native aqueous functional state, laying the groundwork for future clinical studies using more portable devices.

Visible microspectrophotometry coupled with machine learning to discriminate the erythrocytic life cycle stages of P. falciparum malaria parasites in functional single cells.

Malaria was regarded as the most devastating infectious disease of the 21st century until the COVID-19 pandemic. Asexual blood staged parasites (ABS) play a unique role in ensuring the parasite's survival and pathogenesis. Hitherto, there have been no spectroscopic reports discriminating the life cycle stages of the ABS parasite under physiological conditions. The identification and quantification of the stages in the erythrocytic life cycle is important in monitoring the progression and recovery from the disease. In this study, we explored visible microspectrophotometry coupled to machine learning to discriminate functional ABS parasites at the single cell level. Principal Component Analysis (PCA) showed an excellent discrimination between the different stages of the ABS parasites. Support Vector Machine Analysis provided a 100% prediction for both schizonts and trophozoites, while a 92% and 98% accuracy was achieved for predicting control and ring staged infected RBCs, respectively. This work shows proof of principle for discriminating the life cycle stages of parasites in functional erythrocytes using visible microscopy and thus eliminating the drying and fixative steps that are associated with other optical-based spectroscopic techniques.

Ultraviolet/Visible and Near-Infrared Dual Spectroscopic Method for Detection and Quantification of Low-Level Malaria Parasitemia in Whole Blood.

The scourge of malaria infection continues to strike hardest against pregnant women and children in Africa and South East Asia. For global elimination, testing methods that are ultrasensitive to low-level ring-staged parasitemia are urgently required. In this study, we used a novel approach for diagnosis of malaria infection by combining both electronic ultraviolet-visible (UV/vis) spectroscopy and near infrared (NIR) spectroscopy to detect and quantify low-level (1-0.000001%) ring-staged malaria-infected whole blood under physiological conditions uisng Multiclass classification using logistic regression, which showed that the best results were achieved using the extended wavelength range, providing an accuracy of 100% for most parasitemia classes. Likewise, partial least-squares regression (PLS-R) analysis showed a higher quantification sensitivity (R2 = 0.898) for the extended spectral region compared to UV/vis and NIR (R2 = 0.806 and 0.556, respectively). For quantifying different-stage blood parasites, the extended wavelength range was able to detect and quantify all thePlasmodium falciparum accurately compared to testing each spectral component separately. These results demonstrate the potential of a combined UV/vis-NIR spectroscopy to accurately diagnose malaria-infected patients without the need for elaborate sample preparation associated with the existing mid-IR approaches.

A Near-Infrared "Matchbox Size" Spectrometer to Detect and Quantify Malaria Parasitemia.

New point-of-care diagnostic approaches for malaria that are sensitive to low parasitemia, easy to use in a field setting, and affordable are urgently required to meet the World Health Organization's objective of reducing malaria cases and related life losses by 90% globally on or before 2030. In this study, an inexpensive "matchbox size" near-infrared (NIR) spectrophotometer was used for the first time to detect and quantify malaria infection in vitro from isolated dried red blood cells using a fingerpick volume of blood. This the first study to apply a miniaturized NIR device to diagnose a parasitic infection and identify marker bands indicative of malaria infection in the NIR region. An NIR device has many advantages including wavelength accuracy and repeatability, speed, resolution, and a greatly improved signal-to-noise ratio compared to existing spectroscopic options. Using multivariate data analysis, we discriminated control red blood cells from infected cells and established the limit of detection of the technique. Principal component analysis displayed a good separation between the infected and uninfected RBCs, while partial least-squares regression analysis yielded a robust parasitemia prediction with root-mean-square error of prediction values of 0.446 and 0.001% for the higher and lower parasitemia models, respectively. The R2 values of the higher and lower parasitemia models were 0.947 and 0.931, respectively. Finally, an estimated parasitemia detection limit of 0.00001% and a qunatification limit of 0.001% was achieved; to ascertain the true efficacy of the technique for point-of-care screening, clinical studies using large patient numbers are required, which is the subject of future studies.

Infrared Spectroscopy of Blood.

The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field.