Visible and near-infrared spectra collected from the thumbs of patients with chronic fatigue syndrome for diagnosis

Blood-based near-infrared spectroscopy for the rapid low-cost detection of Alzheimer's disease

Alzheimer's disease (AD) is currently under-diagnosed and is predicted to affect a great number of people in the future, due to the unrestrained aging of the population. An accurate diagnosis of AD at an early stage, prior to (severe) symptomatology, is of crucial importance as it would allow the subscription of effective palliative care and/or enrolment into specific clinical trials. Today, new analytical methods and research initiatives are being developed for the on-time diagnosis of this devastating disorder. During the last decade, spectroscopic techniques have shown great promise in the robust diagnosis of various pathologies, including neurodegenerative diseases and dementia. In the current study, blood plasma samples were analysed with near-infrared (NIR) spectroscopy as a minimally-invasive method to distinguish patients with AD (n = 111) from non-demented volunteers (n = 173). After applying multivariate classification models (principal component analysis with quadratic discriminant analysis - PCA-QDA), AD individuals were correctly identified with 92.8% accuracy, 87.5% sensitivity and 96.1% specificity. Our results show the potential of NIR spectroscopy as a simple and cost-effective diagnostic tool for AD. Robust and early diagnosis may be a first step towards tackling this disease by allowing timely intervention.

Near-infrared spectroscopy for medical applications: Current status and future perspectives

The near-infrared radiation (NIR) window, also known as the "optical window" or "therapeutic window", is the range of wavelengths that has the maximum depth of penetration in tissue. Indeed, because NIR is minimally absorbed by water and hemoglobin, spectra readings can be easily collected from the body surface. Recent reports have shown the potential of NIR spectroscopy in various medical applications, including functional analysis of the brain and other tissues, as well as an analytical tool for diagnosing diseases. The broad applicability of NIR spectroscopy facilitates the diagnosis and therapy of diseases as well as elucidating their pathophysiology. This review introduces recent advances and describes new studies in NIR to demonstrate potential clinical applications of NIR spectroscopy.

Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis

Due to an absence of reliable biochemical markers, the diagnosis of chronic fatigue syndrome (CFS) mainly relies on the clinical symptoms, and the experience and skill of the doctors currently. To improve objectivity and reduce work intensity, a hybrid facial feature is proposed. First, several kinds of appearance features are identified in different facial regions according to clinical observations of traditional Chinese medicine experts, including vertical striped wrinkles on the forehead, puffiness of the lower eyelid, the skin colour of the cheeks, nose and lips, and the shape of the mouth corner. Afterwards, such features are extracted and systematically combined to form a hybrid feature. We divide the face into several regions based on twelve active appearance model (AAM) feature points, and ten straight lines across them. Then, Gabor wavelet filtering, CIELab color components, threshold-based segmentation and curve fitting are applied to extract features, and Gabor features are reduced by a manifold preserving projection method. Finally, an AdaBoost based score level fusion of multi-modal features is performed after classification of each feature. Despite that the subjects involved in this trial are exclusively Chinese, the method achieves an average accuracy of 89.04% on the training set and 88.32% on the testing set based on the K-fold cross-validation. In addition, the method also possesses desirable sensitivity and specificity on CFS prediction.

Discrimination of influenza virus-infected nasal fluids by Vis-NIR spectroscopy

BACKGROUND

Influenza patients show a severe condition of the respiratory tract with high temperature. Efficient treatment of influenza requires early use of oseltamivir, and thus rapid diagnosis is needed. Recently, rapid diagnostic methods such as immunochromatography have been developed; however, immunochromatography is not an optimal technique because it is relatively expensive and has low sensitivity.

METHODS

Visible and near-infrared (Vis-NIR) spectroscopy in the region 600-1100 nm, combined with chemometrics analysis such as principal component analysis (PCA) or soft modeling of class analogy (SIMCA), was used to develop a potential diagnostic method for influenza based on nasal aspirates from infected patients.

RESULTS

The Vis-NIR spectra of nasal aspirates from 33 non-influenza patients and 34 influenza patients were subjected to PCA and SIMCA to develop multivariate models to discriminate between influenza and non-influenza patients. These models were further assessed by the prediction of 126 masked measurements [30 from non-influenza patients, 30 from influenza patients and 66 from patients infected with respiratory syncytial virus (RSV)]. The PCA model showed some discrimination of the masked samples. The SIMCA model correctly predicted 29 of 30 (96.7%) non-influenza patients, and 30 of 30 (100%) influenza patients from the Vis-NIR spectra of masked nasal aspirate samples. Nasal aspirates of RSV-infected patients were predicted as 50% non-influenza and 50% influenza by the SIMCA model, suggesting that discrimination between patients infected with influenza virus and those infected with RSV was difficult.

CONCLUSIONS

Although the study sample was small and there was difficulty in discriminating between influenza virus and RSV infection, these results suggest that Vis-NIR spectroscopy of nasal aspirates, combined with chemometrics analysis, might be a potential tool for diagnosis of influenza.