Significance: The rates of melanoma and nonmelanoma skin cancer are rising across the globe. Due to a shortage of board-certified dermatologists, the burden of dermal lesion screening and erythema monitoring has fallen to primary care physicians (PCPs). An adjunctive device for lesion screening and erythema monitoring would be beneficial because PCPs are not typically extensively trained in dermatological care.
Aim: We aim to examine the feasibility of using a smartphone-camera-based dermascope and a USB-camera-based dermascope utilizing polarized white-light imaging (PWLI) and polarized multispectral imaging (PMSI) to map dermal chromophores and erythema.
Approach: Two dermascopes integrating LED-based PWLI and PMSI with both a smartphone-based camera and a USB-connected camera were developed to capture images of dermal lesions and erythema. Image processing algorithms were implemented to provide chromophore concentrations and redness measures.
Results: PWLI images were successfully converted to an alternate colorspace for erythema measures, and the spectral bandwidth of the PMSI LED illumination was sufficient for mapping of deoxyhemoglobin, oxyhemoglobin, and melanin chromophores. Both types of dermascopes were able to achieve similar relative concentration results.
Conclusion: Chromophore mapping and erythema monitoring are feasible with PWLI and PMSI using LED illumination and smartphone-based cameras. These systems can provide a simpler, more portable geometry and reduce device costs compared with interference-filter-based or spectrometer-based clinical-grade systems. Future research should include a rigorous clinical trial to collect longitudinal data and a large enough dataset to train and implement a machine learning-based image classifier.
Oral cancer is one of the most common malignant tumors. There are 354,864 new cases and 177,384 death per year globally according to Globocan 2018 report. Most of the cases are in low- and middle-income countries that lack trained specialists and health services, of which India accounts for approximately one-third of the new cases and two-fifth deaths. Point-of-care oral screening tool to enable early diagnosis is urgently needed. We developed a dual-mode intraoral oral cancer screening platform and an automatic classification algorithm for oral dysplasia and malignancy images using deep learning.
Oral cancer is a growing health issue in low- and middle-income countries due to betel quid, tobacco, and alcohol use and in younger populations of middle- and high-income communities due to the prevalence of human papillomavirus. The described point-of-care, smartphone-based intraoral probe enables autofluorescence imaging and polarized white light imaging in a compact geometry through the use of a USB-connected camera module. The small size and flexible imaging head improves on previous intraoral probe designs and allows imaging the cheek pockets, tonsils, and base of tongue, the areas of greatest risk for both causes of oral cancer. Cloud-based remote specialist and convolutional neural network clinical diagnosis allow for both remote community and home use. The device is characterized and preliminary field-testing data are shared.
Oral cancer is a rising health issue in many low and middle income countries (LMIC). Proposed is an implementation of autofluorescence imaging (AFI) and white light imaging (WLI) on a smartphone platform providing inexpensive early detection of cancerous conditions in the oral cavity. Interchangeable modules allow both whole mouth imaging for an overview of the patients’ oral health and an intraoral imaging probe for localized information. Custom electronics synchronize image capture and external LED operation for the excitation of tissue fluorescence. A custom Android application captures images and an image processing algorithm provides likelihood estimates of cancerous conditions. Finally, all data can be uploaded to a cloud server where a convolutional neural network classifies the images and a remote specialist can provide diagnosis and triage instructions.
The Functional Freeform Fitting (F4) method is utilized to design a freeform optic for oblique illumination of Mark Rothko’s Green on Blue (1956). Shown are preliminary results from an iterative freeform design process; from problem definition and specification development to surface fit, ray tracing results, and optimization. This method is applicable to both point and extended sources of various geometries.
In this paper, laser modules based on newly developed tailored bars are presented. The modules allow efficient fiber coupling of more than 320 W into 10 mm-mrad or 160 W into 6 mm-mrad at one single wavelength. For further power scaling dense wavelength coupling concepts are presented which enable kW-class lasers with a beam quality of 10 mm-mrad.
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