Do it yourself smartphone fundus camera - DIYretCAM.
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ABSTRACT:
SUBMITTER: Raju B
PROVIDER: S-EPMC5151157 | biostudies-literature | 2016 Sep
REPOSITORIES: biostudies-literature
Publications
Do it yourself smartphone fundus camera - DIYretCAM.
Raju Biju B Raju N S D NS Akkara John Davis JD Pathengay Avinash A
Indian journal of ophthalmology 20160901 9
This article describes the method to make a do it yourself smartphone-based fundus camera which can image the central retina as well as the peripheral retina up to the pars plana. It is a cost-effective alternative to the fundus camera. ...[more]
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Project description:PurposeTo compare dilated smartphone-based imaging with a nonmydriatic, tabletop fundus camera as a teleophthalmology screening tool for diabetic retinopathy (DR).MethodsThis was a single-institutional, cross-sectional, comparative-instrument study. Fifty-six patients at a safety-net hospital underwent teleophthalmology screening for DR using standard, nonmydriatic fundus photography with a tabletop camera (Nidek NM-1000) and dilated fundus photography using a smartphone camera with lens adapter (Paxos Scope, Verana Health). Masked graders performed standardized photo grading. Quantitative comparisons were performed employing descriptive, κ, Bland-Altman, and receiver operating characteristic analyses.ResultsPosterior segment photography was of sufficient quality to grade in 89% of mydriatic smartphone-imaged eyes and in 86% of nonmydriatic tabletop camera-imaged eyes (P = .03). Using the tabletop camera as the reference to detect moderate nonproliferative DR or worse (referral-warranted DR), mydriatic smartphone-acquired photographs were found to be 82% sensitive and 96% specific. Dilated smartphone imaging detected referral-warranted DR in 3 eyes whose tabletop camera imaging did not demonstrate referral-warranted DR. Secondary masked review of medical records for the discordances in referral-warranted status from the two imaging modalities was performed, and it revealed revised sensitivity and specificity values of 95% and 98%, respectively. Overall, there was good agreement between tabletop camera and smartphone-acquired photo grades (κ = 0.91 ± 0.1, P < .001; area under the receiver operating characteristic curve = 0.99, 95% CI, 0.98-1.00).ConclusionsMydriatic smartphone-based imaging resulted in fewer ungradable photos compared to nonmydriatic table-top camera imaging and detected more patients with referral-warranted DR. Our study supports the use of mydriatic smartphone teleophthalmology as an alternative method to screen for DR.
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Project description:Fluorescence microscopy is an important technique for cellular and microbiological investigations. Translating this technique onto a smartphone can enable particularly powerful applications such as on-site analysis, on-demand monitoring, and point-of-care diagnostics. Current fluorescence smartphone microscope setups require precise illumination and imaging alignment which altogether limit its broad adoption. We report a multi-color fluorescence smartphone microscope with a single contact lens-like add-on lens and slide-launched total-internal-reflection guided illumination for three common tasks in investigative fluorescence microscopy: autofluorescence, fluorescent stains, and immunofluorescence. The open-source, simple and cost-effective design has the potential for do-it-yourself fluorescence smartphone microscopy.
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Project description:PurposeTo construct a low-cost, easy-to-use, high-image-quality mydriatic fundus camera with "point-and-shoot" operation, and to evaluate the efficacy of this camera to accurately document retinal disease.MethodsA prototype portable fundus camera was designed by interfacing a novel optical module with a Panasonic Lumix G2 consumer camera. Low-cost, commercially available optics were used to create even illumination of the fundus, providing a 50° retinal field of view. A comparative study assessing the image quality of the prototype camera against a traditional tabletop fundus camera was conducted under an Institutional Review Board (IRB)-approved study.ResultsA stand-alone, mydriatic camera prototype was successfully developed at a parts cost of less than $1000. The prototype camera was capable of operating in a point-and-shoot manner with automated image focusing and exposure, and the image quality of fundus photos was comparable to that of existing commercial cameras. Pathology related to both nonproliferative and proliferative diabetic retinopathy and age-related macular degeneration was easily identified from fundus images obtained from the low-cost camera.ConclusionsEarly prototype development and clinical testing have shown that a consumer digital camera can be inexpensively modified to image the fundus with professional diagnostic quality. The combination of low cost, portability, point-and-shoot operation, and high image quality provides a foundational platform on which one can design an accessible fundus camera to screen for eye disease.
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Project description:Out-of-hospital cardiac arrest (OHCA) is recognized as a global mortality challenge, and digital strategies could contribute to increase the chance of survival. In this paper, we investigate if cardiopulmonary resuscitation (CPR) quality measurement using smartphone video analysis in real-time is feasible for a range of conditions. With the use of a web-connected smartphone application which utilizes the smartphone camera, we detect inactivity and chest compressions and measure chest compression rate with real-time feedback to both the caller who performs chest compressions and over the web to the dispatcher who coaches the caller on chest compressions. The application estimates compression rate with 0.5?s update interval, time to first stable compression rate (TFSCR), active compression time (TC), hands-off time (TWC), average compression rate (ACR), and total number of compressions (NC). Four experiments were performed to test the accuracy of the calculated chest compression rate under different conditions, and a fifth experiment was done to test the accuracy of the CPR summary parameters TFSCR, TC, TWC, ACR, and NC. Average compression rate detection error was 2.7 compressions per minute (±5.0?cpm), the calculated chest compression rate was within ±10?cpm in 98% (±5.5) of the time, and the average error of the summary CPR parameters was 4.5% (±3.6). The results show that real-time chest compression quality measurement by smartphone camera in simulated cardiac arrest is feasible under the conditions tested.
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Project description:Body composition is a key component of health in both individuals and populations, and excess adiposity is associated with an increased risk of developing chronic diseases. Body mass index (BMI) and other clinical or commercially available tools for quantifying body fat (BF) such as DXA, MRI, CT, and photonic scanners (3DPS) are often inaccurate, cost prohibitive, or cumbersome to use. The aim of the current study was to evaluate the performance of a novel automated computer vision method, visual body composition (VBC), that uses two-dimensional photographs captured via a conventional smartphone camera to estimate percentage total body fat (%BF). The VBC algorithm is based on a state-of-the-art convolutional neural network (CNN). The hypothesis is that VBC yields better accuracy than other consumer-grade fat measurements devices. 134 healthy adults ranging in age (21-76 years), sex (61.2% women), race (60.4% White; 23.9% Black), and body mass index (BMI, 18.5-51.6 kg/m2) were evaluated at two clinical sites (N = 64 at MGH, N = 70 at PBRC). Each participant had %BF measured with VBC, three consumer and two professional bioimpedance analysis (BIA) systems. The PBRC participants also had air displacement plethysmography (ADP) measured. %BF measured by dual-energy x-ray absorptiometry (DXA) was set as the reference against which all other %BF measurements were compared. To test our scientific hypothesis we run multiple, pair-wise Wilcoxon signed rank tests where we compare each competing measurement tool (VBC, BIA, …) with respect to the same ground-truth (DXA). Relative to DXA, VBC had the lowest mean absolute error and standard deviation (2.16 ± 1.54%) compared to all of the other evaluated methods (p < 0.05 for all comparisons). %BF measured by VBC also had good concordance with DXA (Lin's concordance correlation coefficient, CCC: all 0.96; women 0.93; men 0.94), whereas BMI had very poor concordance (CCC: all 0.45; women 0.40; men 0.74). Bland-Altman analysis of VBC revealed the tightest limits of agreement (LOA) and absence of significant bias relative to DXA (bias -0.42%, R2 = 0.03; p = 0.062; LOA -5.5% to +4.7%), whereas all other evaluated methods had significant (p < 0.01) bias and wider limits of agreement. Bias in Bland-Altman analyses is defined as the discordance between the y = 0 axis and the regressed line computed from the data in the plot. In this first validation study of a novel, accessible, and easy-to-use system, VBC body fat estimates were accurate and without significant bias compared to DXA as the reference; VBC performance exceeded those of all other BIA and ADP methods evaluated. The wide availability of smartphones suggests that the VBC method for evaluating %BF could play an important role in quantifying adiposity levels in a wide range of settings.Trial registration: ClinicalTrials.gov Identifier: NCT04854421.
| S-EPMC9243018 | biostudies-literature
Project description:Purpose. Nonmydriatic fundus cameras allow retinal photography without pharmacologic dilation of the pupil. However, currently available nonmydriatic fundus cameras are bulky, not portable, and expensive. Taking advantage of recent advances in mobile technology, we sought to create a nonmydriatic fundus camera that was affordable and could be carried in a white coat pocket. Methods. We built a point-and-shoot prototype camera using a Raspberry Pi computer, an infrared-sensitive camera board, a dual infrared and white light light-emitting diode, a battery, a 5-inch touchscreen liquid crystal display, and a disposable 20-diopter condensing lens. Our prototype camera was based on indirect ophthalmoscopy with both infrared and white lights. Results. The prototype camera measured 133mm × 91mm × 45mm and weighed 386 grams. The total cost of the components, including the disposable lens, was $185.20. The camera was able to obtain good-quality fundus images without pharmacologic dilation of the pupils. Conclusion. A fully functional, inexpensive, handheld, nonmydriatic fundus camera can be easily assembled from a relatively small number of components. With modest improvements, such a camera could be useful for a variety of healthcare professionals, particularly those who work in settings where a traditional table-mounted nonmydriatic fundus camera would be inconvenient.
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Project description:BackgroundHyperbilirubinemia is a common problem in neonates that can progress into kernicterus. Suspected neonatal hyperbilirubinemia is a common reason for contact with the healthcare system. The severity and management of jaundice are determined based on estimated bilirubin levels. However, no easy and accessible tool for self-assessing neonatal jaundice is currently available. Smartphones could potentially be transformed into a medical device that could be used by both patients and practitioners.ObjectiveTo investigate whether a digital image produced by a camera embedded on a smartphone can be a used as a screening tool for neonatal hyperbilirubinemia.Study designA total of 64 randomly selected newborns were enrolled. The inclusion criteria were healthy Caucasians, gestational age >35 weeks, age >24 hours and ?14 days old, and parental informed consent. The exclusion criteria were facial skin lesions and light treatment. Images of the glabella were obtained with an iPhone 6 via i) directly applied pressure, ii) a dermatoscope, or iii) a dermatoscope equipped with a Wratten No. 11 filter. The red, green and blue colour intensities of each image were compared to bilirubin levels.ResultsOnly the dermatoscope-acquired intensities of the green and blue channels were significantly correlated (p < 0.001) with bilirubin measurements (Pearson's r: 0.59 and 0.48, respectively). For the green and blue channels, discrimination limits of 212 and 190, respectively, revealed a sensitivity and specificity of 100% and 62.5%, respectively, for green and 90.9% and 60%, respectively, for blue for a plasma bilirubin above 205 ?mol/L.ConclusionsThe results of this study indicate that a smartphone equipped with a consistent light source in the form of a dermatoscope may be a simple screening tool for neonatal hyperbilirubinemia. However, the method requires some improvement before clinical application.
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Project description:Objective Direct urine color assessment has been shown to correlate with hydration status. However, this method is subject to inter- and intra-observer variability. Digital image colorimetry provides a more objective method. This study evaluated the diagnostic accuracy of urine photo colorimetry using different smartphones under different lighting conditions, and determined the optimal cut-off value to predict clinical dehydration. Methods The urine samples were photographed in a customized photo box, under five simulated lighting conditions, using five smartphones. The images were analyzed using Adobe Photoshop to obtain Red, Green, and Blue (RGB) values. The correlation between RGB values and urine laboratory parameters were determined. The optimal cut-off value to predict dehydration was determined using area under the receiver operating characteristic curve. Results A total of 56 patients were included in the data analysis. Images captured using five different smartphones under five lighting conditions produced a dataset of 1400 images. The study found a statistically significant correlation between Blue and Green values with urine osmolality, sodium, urine specific gravity, protein, and ketones. The diagnostic accuracy of the Blue value for predicting dehydration were “good” to “excellent” across all phones under all lighting conditions with sensitivity >90% at cut-off Blue value of 170. Conclusions Smartphone-based urine colorimetry is a highly sensitive tool in predicting dehydration.
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