Design of a Low-Cost Diffuse Optical Mammography System for Biomedical Image Processing in Breast Cancer Diagnosis.

Worldwide, breast cancer is the most common type of cancer that mainly affects women. Several diagnosis techniques based on optical instrumentation and image analysis have been developed, and these are commonly used in conjunction with conventional diagnostic devices such as mammographs, ultrasound, and magnetic resonance imaging of the breast. The cost of using these instruments is increasing, and developing countries, whose deaths indices due to breast cancer are high, cannot access conventional diagnostic methods and have even less access to newer techniques. Other studies, based on the analysis of images acquired by traditional methods, require high resolutions and knowledge of the origin of the captures in order to avoid errors. For this reason, the design of a low-cost diffuse optical mammography system for biomedical image processing in breast cancer diagnosis is presented. The system combines the acquisition of breast tissue photographs, diffuse optical reflectance (as a biophotonics technique), and the processing of digital images for the study and diagnosis of breast cancer. The system was developed in the form of a medical examination table with a 638 nm red-light source, using light-emitted diode technology (LED) and a low-cost web camera for the acquisition of breast tissue images. The system is automatic, and its control, through a graphical user interface (GUI), saves costs and allows for the subsequent analysis of images using a digital image-processing algorithm. The results obtained allow for the possibility of planning in vivo measurements. In addition, the acquisition of images every 30° around the breast tissue could be used in future research in order to perform a three-dimensional (3D) reconstruction and an analysis of the captures through deep learning techniques. These could be combined with virtual, augmented, or mixed reality environments to predict the position of tumors, increase the likelihood of a correct medical diagnosis, and develop a training system for specialists. Furthermore, the system allows for the possibility to develop analysis of optical characterization for new phantom studies in breast cancer diagnosis through bioimaging techniques.

In Vitro Antimicrobial Photodynamic Therapy for Pseudomonas aeruginosa (P. aeruginosa) and methicillin-resistant Staphylococcus aureus (MRSA) Inhibition Using a Green Light Source.

Photodynamic therapy (PDT) has been based on using photosensitizers (PS) and applying light of a specific wavelength. When this technique is used for treating infections, it is known as antimicrobial photodynamic therapy (aPDT). Currently, the use of lighting sources for in vitro studies using aPDT is generally applied in multiwell cell culture plates; however, depending on the lighting arrangement, there are usually errors in the application of the technique because the light from a well can affect the neighboring wells or it may be that not all the wells are used in the same experiment. In addition, one must be awarded high irradiance values, which can cause unwanted photothermal problems in the studies. Thus, this manuscript presents an in vitro antimicrobial photodynamic therapy for a Pseudomonas aeruginosa (P. aeruginosa) and methicillin-resistant Staphylococcus aureus (MRSA) inhibition study using an arrangement of thermally isolated and independently illuminated green light source systems for eight tubes in vitro aPDT, determining the effect of the following factors: (i) irradiance level, (ii) exposure time, and (iii) Rose Bengal (RB) concentration (used as a PS), registering the Pseudomonas aeruginosa (P. aeruginosa) and methicillin-resistant Staphylococcus aureus (MRSA) inhibition rates. The results show that in the dark, RB had a poor antimicrobial rate for P. aeruginosa, finding the maximum inhibition (2.7%) at 30 min with an RB concentration of 3 µg/mL. However, by applying light in a correct dosage (time × irradiance) and the adequate RB concentration, the inhibition rate increased by over 37%. In the case of MRSA, there was no significant inhibition with RB in complete darkness and, in contrast, the rate was 100% for those experiments that were irradiated.

Combined methods of optical spectroscopy and artificial intelligence in the assessment of experimentally induced non-alcoholic fatty liver.

BACKGROUND AND OBJECTIVE: Due to the existing prevalence of nonalcoholic fatty liver disease (NAFLD) and its relation to the epidemic of obesity in the general population, it is imperative to develop detection and evaluation methods of the early stages of the disease with improved efficacy over the current diagnostic approaches. We aimed to obtain an improved diagnosis, combining methods of optical spectroscopy -diffuse reflectance and fluorescence- with statistical data analysis applied to detect early stages of NAFLD. METHODS: Statistical analysis scheme based on quadratic discriminant analysis followed by canonical discriminant analysis were applied to the diffuse reflectance data combined with endogenous fluorescence spectral data excited at one of these wavelengths: 330, 365, 385, 405 or 415 nm. The statistical scheme was also applied to the combinations of fluorescence spectrum (405 nm) with each one of the other fluorescence spectra. Details of the developed software, including the application of machine learning algorithms to the combination of spectral data followed by classification statistical schemes, are discussed. RESULTS: Steatosis progression was differentiated with little classification error (≤1.3%) by using diffuse reflectance and endogenous fluorescence at different wavelengths. Similar results were obtained using fluorescence at 405 nm and one of the other fluorescence spectra (classification error ≤1.0%). Adding the corresponding areas under the curves to the above combinations of spectra diminished errors to 0.6% and 0.3% or less, respectively. The best results for the compounded reflectance-plus-fluorescence spectra were obtained with fluorescence spectra excited at 415 nm with a total classification error of 0.2%; for the combination of the 405nm-excited fluorescence spectrum with another fluorescence spectrum, the best results were achieved for 385 nm, for which total relative classification error amounted 0.4%. The consideration of the area under the spectral curves further improved both classifiers, reducing the error to 0.0% in both cases. CONCLUSION: Spectrometric techniques combined with statistical processing are a promising tool to improve steatosis classification through a label free approach. However, statistical schemes here applied, might result complex for the everyday medical practice, the designed software including machine learning algorithms is able to render automatic classification of samples according to their steatosis grade with low error.

Fluorescence spectroscopy on paraffin-preserved human liver samples to classify several grades of fibrosis.

Nowadays, it is well established that biopsy is the gold standard for medical diagnosis of liver disease; however, recent studies have shown numerous discrepancies in biopsy assessment, even when it is evaluated by senior pathologists. Fluorescence spectroscopy is a tool that has been of utility in the diagnosis of different diseases based on biopsy analysis. Thus, fluorescence study of liver samples with five different degrees of fibrosis is presented. Paraffin-preserved human liver tissue was provided on white plastic cassettes by the Hospital General de Mexico "Dr. Eduardo Liceaga". Specimens were diagnosed by two independent-senior pathologists in a double-blind test and classified into five different groups: F0, F1, F2, F3, and F4, according to the METAVIR scale for liver fibrosis. Fluorescence spectroscopy measurements were performed using three different excitation wavelengths: 385, 405, and 450 nm. Besides, diffuse reflectance spectroscopy (DRS) measurements were taken with white light to determine morphological changes in the tissue and to compare the results with medical diagnosis. The spectral analysis at excitation wavelengths of 385 nm and 405 nm showed poor correlation with medical diagnosis. Likewise, in order to discard all possible error-sources involved in the measurements, an exhaustive study was carried out; it included the determination of the fluorescence noise produced by paraffin, cassette, and the tissue itself. At 450 nm excitation wavelength, no fluorescence by the cassette was detected and noise-subtraction methods were not required, this allows a high correlation of hepatic fibrosis stages between pathological diagnosis and spectroscopic analysis. For this excitation wavelength, 89.87% correlation with DRS measurements and 82.00% with medical diagnosis were obtained. This work demonstrates that fluorescence spectroscopy using 450 nm excitation wavelength might work as a complementary tool to grade hepatic fibrosis in human liver specimens.