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.

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.

Improved costs and outcomes with conscious sedation vs general anesthesia in TAVR patients: Time to wake up?

BACKGROUND: Transcatheter aortic valve replacement (TAVR) has become a commonplace procedure for the treatment of aortic stenosis in higher risk surgical patients. With the high cost and steadily increasing number of patients receiving TAVR, emphasis has been placed on optimizing outcomes as well as resource utilization. Recently, studies have demonstrated the feasibility of conscious sedation in lieu of general anesthesia for TAVR. This study aimed to investigate the clinical as well as cost outcomes associated with conscious sedation in comparison to general anesthesia in TAVR. METHODS: Records for all adult patients undergoing TAVR at our institution between August 2012 and June 2016 were included using our institutional Society of Thoracic Surgeons (STS) and American College of Cardiology (ACC) registries. Cost data was gathered using the BIOME database. Patients were stratified into two groups according to whether they received general anesthesia (GA) or conscious sedation (CS) during the procedure. No-replacement propensity score matching was done using the validated STS predicted risk of mortality (PROM) as a propensity score. Primary outcome measure with survival to discharge and several secondary outcome measures were also included in analysis. According to our institution's data reporting guidelines, all cost data is presented as a percentage of the general anesthesia control group cost. RESULTS: Of the 231 patients initially identified, 225 (157 GA, 68 CS) were included for analysis. After no-replacement propensity score matching, 196 patients (147 GA, 49 CS) remained. Overall mortality was 1.5% in the matched population with a trend towards lower mortality in the CS group. Conscious sedation was associated with significantly fewer ICU hours (30 vs 96 hours, p = <0.001) and total hospital days (4.9 vs 10.4, p<0.001). Additionally, there was a 28% decrease in direct cost (p<0.001) as well as significant decreases in all individual all cost categories associated with the use of conscious sedation. There was no difference in composite major adverse events between groups. These trends remained on all subsequent subgroup analyses. CONCLUSION: Conscious sedation is emerging as a safe and viable option for anesthesia in patients undergoing transcatheter aortic valve replacement. The use of conscious sedation was not only associated with similar rates of adverse events, but also shortened ICU and overall hospital stays. Finally, there were significant decreases in all cost categories when compared to a propensity matched cohort receiving general anesthesia.