Dynamic thermal imaging (DTI) with infrared cameras is a non-invasive technique with the ability to detect the most common types of skin cancer. We discuss and propose a standardized analysis method for DTI of actual patient data, which achieves high levels of sensitivity and specificity by judiciously selecting pixels with the same initial temperature. This process compensates the intrinsic limitations of the cooling unit and is the key enabling tool in the DTI data analysis. We have extensively tested the methodology on human subjects using thermal infrared image sequences from a pilot study conducted jointly with the University of New Mexico Dermatology Clinic in Albuquerque, New Mexico (ClinicalTrials ID number NCT02154451). All individuals were adult subjects who were scheduled for biopsy or adult volunteers with clinically diagnosed benign condition. The sample size was 102 subjects for the present study. Statistically significant results were obtained that allowed us to distinguish between benign and malignant skin conditions. The sensitivity and specificity was 95% (with a 95% confidence interval of [87.8% 100.0%]) and 83% (with a 95% confidence interval of [73.4% 92.5%]), respectively, and with an area under the curve of 95%. Our results lead us to conclude that the DTI approach in conjunction with the judicious selection of pixels has the potential to provide a fast, accurate, non-contact, and non-invasive way to screen for common types of skin cancer. As such, it has the potential to significantly reduce the number of biopsies performed on suspicious lesions.
Skin cancer is the most common cancer in the United States with over 3.5M annual cases. Presently, visual inspection by a dermatologist has good sensitivity (> 90%) but poor specificity (< 10%), especially for melanoma, which leads to a high number of unnecessary biopsies. Here we use dynamic thermal imaging (DTI) to demonstrate a rapid, accurate and non-invasive imaging system for detection of skin cancer. In DTI, the lesion is cooled down and the thermal recovery is recorded using infrared imaging. The thermal recovery curves of the suspected lesions are then utilized in the context of continuous-time detection theory in order to define an optimal statistical decision rule such that the sensitivity of the algorithm is guaranteed to be at a maximum for every prescribed false-alarm probability. The proposed methodology was tested in a pilot study including 140 human subjects demonstrating a sensitivity in excess of 99% for a prescribed specificity in excess of 99% for detection of skin cancer. To the best of our knowledge, this is the highest reported accuracy for any non-invasive skin cancer diagnosis method. 1, 93-112 (1948). 35. G. Shi, F. Han, L. Wang, C. Liang, and K. Li, "Q-r curve of thermal tomography and its clinical application on breast tumor diagnosis," Biomed. Opt. Express 6, 1109-1123 (2015). -differentiating facts from fiction, and the impact of high precision quantum well infrared photodetector camera systems, and other factors, in its reemergence," Infrared Phys.
Gold nanoparticles (AuNPs) are currently under intense investigation for biomedical and biotechnology applications, thanks to their ease in preparation, stability, biocompatibility, multiple surface functionalities, and size-dependent optical properties. The most commonly used method for AuNP synthesis in aqueous solution is the reduction of tetrachloroauric acid (HAuCl4) with trisodium citrate. We have observed variations in the pH and in the concentration of the gold colloidal suspension synthesized under standard conditions, verifying a reduction in the reaction yield by around 46% from pH 5.3 (2.4 nM) to pH 4.7 (1.29 nM). Citrate-capped AuNPs were characterized by UV-visible spectroscopy, TEM, EDS, and zeta-potential measurements, revealing a linear correlation between pH and the concentration of the generated AuNPs. This result can be attributed to the adverse effect of protons both on citrate oxidation and on citrate adsorption onto the gold surface, which is required to form the stabilization layer. Overall, this study provides insight into the effect of the pH over the synthesis performance of the method, which would be of particular interest from the point of view of large-scale manufacturing processes.
While quantum dots-in-a-well (DWELL) infrared photodetectors have the feature that their spectral responses can be shifted continuously by varying the applied bias, the width of the spectral response at any applied bias is not sufficiently narrow for use in multispectral sensing without the aid of spectral filters. To achieve higher spectral resolutions without using physical spectral filters, algorithms have been developed for post-processing the DWELL's bias-dependent photocurrents resulting from probing an object of interest repeatedly over a wide range of applied biases. At the heart of these algorithms is the ability to approximate an arbitrary spectral filter, which we desire the DWELL-algorithm combination to mimic, by forming a weighted superposition of the DWELL's non-orthogonal spectral responses over a range of applied biases. However, these algorithms assume availability of abundant DWELL data over a large number of applied biases (>30), leading to large overall acquisition times in proportion with the number of biases. This paper reports a new multispectral sensing algorithm to substantially compress the number of necessary bias values subject to a prescribed performance level across multiple sensing applications. The algorithm identifies a minimal set of biases to be used in sensing only the relevant spectral information for remote-sensing applications of interest. Experimental results on target spectrometry and classification demonstrate a reduction in the number of required biases by a factor of 7 (e.g., from 30 to 4). The tradeoff between performance and bias compression is thoroughly investigated.
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