Type 2 diabetes mellitus (DM2) is one of the most widely prevalent diseases worldwide and is currently screened by invasive techniques based on enzymatic assays that measure plasma glucose concentration in a laboratory setting. A promising plan of action for screening DM2 is to identify molecular signatures in a non-invasive fashion. This work describes the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, to discern between diabetic patients and healthy controls (Ctrl), with a high degree of accuracy. Using artificial neural networks (ANN), we accurately discriminated between DM2 and Ctrl groups with 88.9–90.9% accuracy, depending on the sampling site. In order to compare the ANN performance to more traditional methods used in spectroscopy, principal component analysis (PCA) was carried out. A subset of features from PCA was used to generate a support vector machine (SVM) model, albeit with decreased accuracy (76.0–82.5%). The 10-fold cross-validation model was performed to validate both classifiers. This technique is relatively low-cost, harmless, simple and comfortable for the patient, yielding rapid diagnosis. Furthermore, the performance of the ANN-based method was better than the typical performance of the invasive measurement of capillary blood glucose. These characteristics make our method a promising screening tool for identifying DM2 in a non-invasive and automated fashion.

Optical antennas have been proposed as an alternative option for solar energy harvesting. In this work the power conversion efficiency of broadband antennas, log-periodic, square-spiral, and archimedian-spiral antennas, coupled to Metal-Insulator-Metal and Esaki rectifying diodes has been obtained from both theoretical and numerical simulation perspectives. The results show efficiencies in the order of 10⁻⁶ to 10⁻⁹ for these rectifying mechanisms, which is very low for practical solar energy harvesting applications. This is mainly caused by the poor performance of diodes at the given frequencies and also due to the antenna-diode impedance mismatch. If only losses due to antenna-diode impedance mismatch are considered an efficiency of about 10⁻³ would be obtained. In order to make optical antennas useful for solar energy harvesting new rectification devices or a different harvesting mechanism should be used.

Infrared thermography is a technique that can detect anomalies in temperature patterns which can indicate some breast pathologies including breast cancer. One limitation of the method is the absence of standardised thermography interpretation procedures. Deep learning models have been used for pattern recognition and classification of objects and have been adopted as an adjunct methodology in medical imaging diagnosis. In this paper, the use of a deep convolutional neural network (CNN) with transfer learning is proposed to automatically classify thermograms into two classes (normal and abnormal). A population of 311 female subjects was considered analysing two approaches to test the CNN’s performance: one with a balanced class distribution and the second study in a typical screening cohort, with a low prevalence of abnormal thermograms. Results showed that the transfer-learned ResNet-101 model had a sensitivity of 92.3% and a specificity of 53.8%, while with an unbalanced distribution the values were 84.6% and 65.3%, respectively. These results suggest that the model presented in this work can classify abnormal thermograms with high sensitivity which validates the use of infrared thermography as an adjunct method for breast cancer screening.

The clinical evaluation of a burn wound alone may not be adequate to predict the severity of the injury nor to guide clinical decision making. Infrared thermography provides information about soft tissue viability and has previously been used to assess burn depth. The objective of this study was to determine if temperature differences in burns assessed by infrared thermography could be used predict the treatment modality of either healing by re-epithelization, requiring skin grafts, or requiring amputations, and to validate the clinical predication algorithm in an independent cohort. Temperature difference (ΔT) between injured and healthy skin were recorded within the first three days after injury in previously healthy burn patients. After discharge, the treatment modality was categorized as re-epithelization, skin graft or amputation. Potential confounding factors were assessed through multiple linear regression models, and a prediction algorithm based on the ΔT was developed using a predictive model using a recursive partitioning Random Forest machine learning algorithm. Finally, the prediction accuracy of the algorithm was compared in the development cohort and an independent validation cohort. Significant differences were found in the ΔT between treatment modality groups. The developed algorithm correctly predicts into which treatment category the patient will fall with 85.35% accuracy. Agreement between predicted and actual treatment for both cohorts was weighted kappa 90%. Infrared thermograms obtained at first contact with a wounded patient can be used to accurately predict the definitive treatment modality for burn patients. This method can be used to rationalize treatment and streamline early wound closure.

Digital infrared thermal imaging is used to assess noninvasively the severity of burn wounds in 13 pediatric patients. A delta-$T$ ($ΔT$) parameter obtained by subtracting the temperature of a healthy contralateral region from the temperature of the burn wound is compared with the burn depth measured histopathologically. Thermal imaging results show that superficial dermal burns (IIa) show increased temperature compared with their contralateral healthy region, while deep dermal burns (IIb) show a lower temperature than their contralateral healthy region. This difference in temperature is statistically significant ($p<0.0001$) and provides a way of distinguishing deep dermal from superficial dermal burns. These results show that digital infrared thermal imaging could be used as a noninvasive procedure to assess burn wounds. An additional advantage of using thermal imaging, which can image a large skin surface area, is that it can be used to identify regions with different burn depths and estimate the size of the grafts needed for deep dermal burns.