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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| S-EPMC5777875 | biostudies-literature
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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| S-EPMC6836609 | biostudies-literature
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.
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