This PDF file contains the front matter associated with SPIE Proceedings Volume 11950 including the Title Page, Copyright information, and Table of Contents.

Fluorescence microscopy is a powerful biological technique employed in a variety of diagnostic assays. Fluorescence based cell counting is a common way of diagnosing various infections. Despite the high sensitivity and specificity offered by fluorescence microscopy; its utility is restricted as it requires trained personnel to operate instrument and interpret results. Commercially available automated fluorescence microscopy is bulky and expensive. We have developed an automated and compact total internal reflection fluorescence (TIRF) microscopy system, referred to as Miniscope. The Miniscope is a portable, automated, and inexpensive fluorescence based optical system building on a previously reported compact 3D printed TIRF module containing a laser and a prism for a TIRF based sample illumination. The system consists of an aspherical lens, an emission filter, a CMOS camera, a Raspberry Pi, and a micro-stepper motor. It offers automated scanning over an area of 20x20mm² with an optical resolution of 0.76μm/pixel. It can be operated in bright field as well as TIRF mode for image acquisition. As an initial proof-of-concept we have demonstrated its performance using fluorescently labelled HL60 cells spiked in milk samples. Our Miniscope is suitable for providing field deployable diagnostic solutions in resource limited settings due to its compactness, overall low cost and appreciable magnification and resolution.

WGM sensors are a class of optical sensors in which light is tightly confined due to optical resonance within a circular geometry. Expensive tunable diode lasers are typically used to excite resonance in a WGM device, which can be a cost limiting factor in developing economies. Herein, we developed a “reverse tuning” method that eliminates the need for such expensive laser sources. We show the microbubble resonator (MBR), a sub class of WGM devices, is ideally suited for the reverse tuning method by tuning with temperature, pressure and refractive index changes. Reducing the cost of the MBR platform, by utilizing less expensive laser sources with fixed wavelength, increases the practicality of these devices for use in low resource settings. We expect our methods to reduce the total cost of the sensing platform from thousands to a few hundred dollars.

Rates of melanoma mortality are greater in remote areas than in cities. In the United States early-stage melanoma incidence was slightly higher in cities while later stage incidence with metastatic spread was higher in rural areas, suggesting a diagnosis gap between the two. Early detection is key as the 5-year survival rate with early discovery can reach 99%, but as the cancer spreads this survival rate is reduced to 27%. This problem is exacerbated by the inaccessibility of state-of-the-art medical care in remote areas. Thus, an affordable, easily operated, tool could help close the diagnostic gap. Previous studies show melanoma’s reflective and fluorescent spectral signature to be distinct from that of healthy skin, which could be utilized for in-vivo skin cancer detection. Here we present a smartphone spectroscopy system as a tool for melanoma screening with the aim of bringing point-of-care testing to remote areas. The spectrometer consists of a fiber optic cable, collimator and diffraction grating which couple directly to the smartphone camera, detecting a spectrum in the range of 380-650nm. The spectral data is analyzed and displayed by a custom developed phone application, which uses the smartphone’s integrated computing to extract and calibrate the spectrum. Key spectral features tied to melanoma can then be input into a classification model, with the aim of providing a noninvasive rapid optical biopsy. The results are promising; preliminary validation of the system is conducted; next steps include collection of a robust training data set of skin samples.

Cold-chain storage can be challenging and expensive for the transportation and storage of biologics, especially in low-resource settings. Recent research has demonstrated that anhydrous preservation in a trehalose amorphous solid matrix offers an alternative to freeze drying for the preservation of biologics. We have previously described a new processing technique, light assisted drying (LAD), to create trehalose preservation matrices of small volume (40 μL) samples. LAD uses illumination by near-infrared laser light to selectively heat water and speed dehydration. In this study we apply the LAD technique to large volume samples (250 μL) that are more comparable to therapeutic doses.

Several types of the cuffless blood pressure (BP) devices have been developed to calculate BP values mainly from the heart rate (HR). But the cuffless devices suffer from low accuracy compared to the BP values directly measured using sphygmomanometer a with inflatable cuff. Since cuffless BP estimated by optical technology is affected not only by HR but also by vascular resistance, vascular resistance can cause inaccuracies. Vascular resistance is determined by the stiffness of the blood vessels and the air temperature. To examine how air temperature affects the fingertip BP, we prototyped a Fingertip BP device with a non-contact thermometer for measuring outside temperature. This presentation clarifies the findings during the trial production.

Existing method of surveying supermicroplastics (fragments ≤ 350 µm) in sea remains a challenge. To this end, we propose a new method based on laser speckles and two submersible spheres. In simulation experiments, a 630 nm laser illuminated a cuvette containing polystyrene particles and zooplanktons producing speckles recorded by a CMOS camera. Speckles were analyzed to discriminate different sized polystyrene spheres (2 μm, 20 μm and 200 μm) and zooplanktons. As discrimination algorithms, difference of subsequent frames of the speckle movie and deep learning were investigated. Deep learning was found to be capable of distinguishing speckles from different particle sizes.

Cervical cancer disproportionately hurts underserved women from disadvantaged communities. Automated visual evaluation (AVE), which analyzes white light cervical images using machine learning, is being considered for management of screen-positive patients. Gaussian noise was identified as degrading AVE performance. Two noise correction approaches were tested on images from historic data with added Gaussian noise. One denoising method (VDNet) was based on neural networks; the other used conventional Gaussian blur filtering. Images were evaluated by an object detection network (RetinaNet), and by a binary pathology ResNeSt classifier. VDNet filtering limited AVE performance degradation at higher noise levels, while Guassian blur only worked on low noise levels.

Lateral flow assays (LFA’s) are a common diagnostic test form, particularly in low-to-middle income countries (LMIC’s). Visual interpretation of LFA’s can be subjective and inconsistent, especially with faint positive results, and commercial readers are expensive and challenging to implement in LMIC’s. We report a phone-agnostic Android app to acquire images and interpret results of a variety of LFA’s with no additional hardware. Starting from the open-source “rdt-scan” codebase, we integrated new features and revamped the peak detection method. This included improved perspective corrections, phone level check to eliminate shadows, high resolution still-image capture besides existing video frame capture, and new peak detection method. This peak detection incorporated smoothing and baseline removal from the one-dimensional profiles of a given color channel’s intensity averaged across the read window’s width, with location and relative size constraints to correctly report locations and peak heights of control and test lines. The app was tested in a real-world setting in conjunction with an open-access LFA for SARS-CoV-2 antigen developed by GH Labs. The app acquired 155 images of LFA cassettes, and results were compared against both visual interpretation by trained clinical staff and PCR results from the same patients. With an appropriate setting for test line intensity threshold, the app matched visual read for all cases but one missed visual positive. From ROC analyses against PCR, the app outperformed visual read by 1-3% across sensitivity, specificity, and AUC. The app thus demonstrated promise for accurate, consistent interpretation of LFA’s while generating digital records that could also be useful for health surveillance.

In our laboratory, we are currently developing laser nerve stimulation using 1500 nm laser radiation, specifically as a potential alternative to electrical stimulation of the vagus nerve. The newly emerging stimulation method may have significant advantages over conventional electrical nerve stimulation for scientific studies and clinical applications: (1) non-contact delivery of external stimulus signals at mm scaled distance in air, (2) enhanced spatial selectivity, and (3) electrical artifact-free measurements. However, one of the issues limiting these advantages is that the tissue temperature is trapped in a narrow window (41°C – 48°C) for a successful and safe laser nerve stimulation. Another limitation is that only instantaneous surface temperature measurement is possible. This report presents to design a scope head that delivers a 1290 nm OCT beam for immediate backscatter coefficient feedback, a 1500 nm laser beam for laser nerve stimulation, and provides real-time imaging. The preliminary test of the scope head is described as fictitious.

Sensors based on the Local Surface plasmon Resonance (LSPR) are attractive due to their simple structure and good sensitivity, but the expensive optoelectronic part of the device is limiting the practical applications. There is a need for new strategies to bring the excellent detection properties of LSPR sensors to the playground of low-cost devices and materials. In this work, it is proposed a novel approach to the output extraction of from LSPR sensor whose sensing element is composed by metal nanoparticles (MNPs). Illuminated with an incident broad light source, the sensor produces a spectral transmission output where the MNPs act like a band-stop optical filter for a specific wavelength. An alteration of the refractive index in the surrounding medium corresponds directly to a shift of the filtering rejection band, which corresponds to a slight change in the colour of the light transmitted by the sensor elements. This colour change can be captured by a CMOS photo-camera, used as an image sensor. It is proposed in this paper an approach based on an automatized image processing algorithm for colour change detection, yielding to a system capable of detecting refractive index variations, avoiding the use of expensive spectrometers. The algorithm comprises three stages: (1) Region of interest detection: images are first cropped using the Otsu threshold binary image to remove the uninteresting areas in the image. (2) Image segmentation: using the watershed algorithm, the sensor elements (sample) area is detected automatically in the cropped image. The segmentation is done using the gradient image, where the watershed markers are the regions of low gradient and barriers are the areas of high values inside the image. (3) The resulted sample region is then processed to find its average or dominant LAB colour and then compare it to its corresponding sample image immersed in different mediums using the colour difference measurement CIEDE2000.