Samsung S8 Research Articles

Comparing deep learning models for tuberculosis detection: A retrospective study of digital vs. analog chest radiographs

PurposeChest X-ray (CXR) is an essential tool and one of the most prescribed imaging to detect pulmonary abnormalities, with a yearly estimate of over 2 billion imaging performed worldwide. However, the accurate and timely diagnosis of TB remains an unmet goal. The prevalence of TB is highest in low-middle-income countries, and the requirement of a portable, automated, and reliable solution is required. In this study, we compared the performance of DL-based devices on digital and analog CXR. The evaluated DL-based device can be used in resource-constraint settings. MethodsA total of 10,000 CXR DICOMs (.dcm) and printed photos of the films acquired with three different cellular phones - Samsung S8, iPhone 8, and iPhone XS along with their radiological report were retrospectively collected from various sites across India from April 2020 to March 2021. Results10,000 chest X-rays were utilized to evaluate the DL-based device in identifying radiological signs of TB. The AUC of qXR for detecting signs of tuberculosis on the original DICOMs dataset was 0.928 with a sensitivity of 0.841 at a specificity of 0.806. At an optimal threshold, the difference in the AUC of three cellular smartphones with the original DICOMs is 0.024 (2.55%), 0.048 (5.10%), and 0.038 (1.91%). The minimum difference demonstrates the robustness of the DL-based device in identifying radiological signs of TB in both digital and analog CXR. ConclusionThe DL-based device underwent evaluation using 10,000 chest X-rays to identify radiological signs of tuberculosis. When tested on the original DICOMs dataset, the device achieved an Area Under the Curve (AUC) of 0.928, with a sensitivity of 0.841 and a specificity of 0.806. Comparing the device's performance with three cellular smartphones on the same original DICOMs dataset, the AUC differences were found to be 0.024 (2.55%), 0.048 (5.10%), and 0.038 (1.91%) at optimal thresholds. These results highlight the device's robustness in detecting radiological signs of TB, showcasing consistent performance across both digital and analog chest X-rays. The smallest difference in AUC further underscores the device's reliability in accurately identifying TB indicators, reaffirming its potential clinical utility.

Accuracy assessment of smart devices for Geoscience field mapping

We present the measurement of fractures near the town of Beaufort West, South Africa. A field visit was conducted to examine the dip and azimuth of rock outcrops in and around the town. The locations of these various fractures were mapped and their orientation, which included the dip and strike of the rock surface, was measured using a geological compass (i.e., Brunton Truarc 15 Compass). The geological compass measurements were then compared to three mobile devices. These mobile devices, namely an iPad 2 and two smartphones (Samsung S8 and Huawei P10 Lite), all had the same application for standardization and the mobile device results were individually compared to the geological compass. The data stemming from the various mobile devices and the geological compass were then compared in terms of their variance. This statistical analysis was performed using the Correlated T-test method, as well as the Pearson Correlation Coefficient formula. To visually examine the main fracture orientations, the data obtained using the geological compass was plotted on a rose diagram. Results show that the relationship between the geological compass and the mobile device readings had little to no correlation, when using both the correlation and t-tests as combined determinants. This highlights the importance of ensuring measurement accuracy in the field as well as instrument calibration in situ.

Enhancing the performance of optical camera communication via accumulative sampling.

Deemed as a practical approach to realize Visible Light Communication on commercial-off-the-shelf devices, the Optical Camera Communication (OCC) is attracting increasing attention, thanks to its readiness to be built purely upon ubiquitous LED illuminating infrastructure and handy smartphones. However, limited by the low sampling ability of the built-in camera on a smartphone, the performance of existing OCC systems is still far away from the requirements of practical applications. To this end, we further investigate the reception ability of the smartphone's camera and propose an accumulative sampling scheme to improve the performance of the OCC system. Essentially, the proposed scheme can use all the grayscale information of the pixels projected by the LED transmitter, whereas the conventional ones normally use single row (or column) pixels for demodulating. By implementing the lightweight demodulation algorithm with accumulative sampling, we experimentally verify its effectiveness for supporting higher transmission frequency hence better performance in terms of data rate. Extensive evaluations have shown the BERs of the proposed method are over 87% and 96% lower than that provided by the baselines at a maximum transmission frequency of 5 kHz for the Samsung S8 and iPhone 8 Plus receivers, respectively.

Reliability of Smartphone Pulse Oximetry in Subjects at Risk for Hypoxemia.

Pulse oximeters are used to measure [Formula: see text] and pulse rate. These devices are either standalone machines or integrated into physiologic monitoring systems. Some smartphones now have pulse oximetry capabilities. Because it is possible that some patients might utilize this technology, we sought to assess the accuracy and usability of smartphone pulse oximeters. This was a prospective, observational study that involved noninvasive measurements of [Formula: see text] and heart rate with 3 devices: Masimo Radical-7, Kenek Edge with the Apple iPhone 6S, and the Samsung S8 smartphone. Ambulatory adult patients visiting our institution's pulmonary function lab for a 6-min walk test were eligible to participate in the study. Pretest and posttest results for each subject were obtained simultaneously using all 3 devices. All results were analyzed with the Spearman rho correlation test, and Bland-Altman plots were used to assess the agreement of measures between the devices. Forty-seven subjects were enrolled in the study, with pulmonary hypertension (30%) and COPD (23%) being the 2 major diagnoses. The mean ± SD difference between the Masimo and Apple devices for pretest [Formula: see text] was 2.3 ± 2.4%, and the difference for posttest [Formula: see text] was 2.1 ± 3.9%. The mean difference between the Masimo and Samsung devices for pretest [Formula: see text] was 3.2 ± 2.8%, and the difference for posttest [Formula: see text] was 2.4 ± 3.5%. The number of subjects who were unable to obtain [Formula: see text] was higher with the Samsung device than with the Apple device in both pretest (14 of 47 vs 3 of 47) and posttest (17 of 47 vs 5 of 47). In contrast, the Masimo device was able to measure [Formula: see text] in all subjects. Smartphone pulse oximeters were unreliable compared to a hospital pulse oximeter. Further research is needed with evolving technology to better understand smartphone pulse oximetry. (ClinicalTrials.gov registration NCT03534271.).

Accurate device-independent colorimetric measurements using smartphones.

Smartphones provide an ideal platform for colorimetric measurements due to their low cost, portability and image quality. As with any imaging-based colorimetry system, ambient light and device variations introduce error which must be dealt with. We propose a novel processing method consisting of a one-time calibration stage to account for inter-phone variations, and an innovative use of ambient light subtraction with image pairs to account for variation in ambient light. Data collection is kept very simple, making it particularly useful for use in the field, since nothing additional is required in the images. Ambient subtraction is first demonstrated for a range of colors and phones (Samsung S8 and LG Nexus 5X), and the Subtracted Signal to Noise Ratio (SSNR) is defined as a metric for assessing whether an image pair is appropriate at the time of image capture. The experimentally determined SSNR threshold below which to suggest retaking the images is 3.4. The classification accuracy for results using the proposed calibration pipeline is then compared to the simplest image metadata-based alternative and is found to be greatly superior. Finally, a custom colorcard is shown to improve the accuracy of device-independent results for known smaller ranges of colors over a standard colorcard, making this a possible application-specific modification to the overall processing pipeline.

Paper-based smartphone chemosensor for reflectometric on-site total polyphenols quantification in olive oil

High polyphenols levels are one of the main quality characters of Extra Virgin Olive Oil (EVOO), leading to high antioxidant activity and contributing to the EVOO health beneficial effects. Analytical methods for the rapid assessment of EVOO polyphenols content, possibly directly at the place of production or distribution, are therefore important for the commercial enhancement of this product. Unfortunately, most analytical methods for quantification of polyphenols in EVOO require laboratory instrumentation and preliminary sample extraction procedures. In this paper we describe a paper-based chemosensor based on the well-known Folin-Ciocalteu colorimetric assay for the rapid (assay time 15 min) quantitative detection of polyphenols in EVOO without any preliminary sample extraction. Use of n-propanol for diluting the EVOO sample allows its direct analysis on paper supports loaded with the Folin-Ciocalteu reagent. The color change of the paper supports is measured using a smartphone camera. A disposable analytical cartridge containing all necessary reagents, including calibration standards, and accessories for performing the assay using a Samsung S8 smartphone have been developed to perform on-site analysis. Measurement of total polyphenol content of EVOO samples gave results in good agreement with the conventional Folin-Ciocalteu assay, and the limit of detection was 30 μg gallic acid equivalents g−1 EVOO. The same approach can be employed to measure polyphenols in other oils of vegetal origin.

Semiotic Analysis of ??Samsung Galaxy S8: The Travel Guide’’ Video Advertisement

This study is entitled ?? Semiotic Analysis of Samsung S8: The Travel Guide Video Advertisement.’’ the discussion of this study focuses on the verbal and visual signs. This study aimed at identifying the meaning of verbal sign and analyzing the visual sign contained in the Samsung S8: The Travel Guide Video Advertisement. The method of conducting data in the study was documentation method. The collected data were analyzed by using descriptive qualitative method according the theory of meaning by Leech (1983) to analyze the meanings of verbal signs and the theory of visual sign communication proposed by Dyer (1993) which is aimed to analyze the elements of visual sign. The result of the analysis shows verbal signs in the spoken implied meanings that want to be conveyed by the narrator. And the visual sign in the video advertisement has the elements which included some categories such appearance, manner, activity, props, setting, and analyzing photograph or images.

The Endockscope Using Next Generation Smartphones: "A Global Opportunity".

The Endockscope combines a smartphone, a battery powered flashlight and a fiberoptic cystoscope allowing for mobile videocystoscopy. We compared conventional videocystoscopy with the Endockscope paired with next generation smartphones in an ex-vivo porcine bladder to evaluate its image quality. The Endockscope consists of a three-dimensional (3D) printed attachment that connects a smartphone to a flexible fiberoptic cystoscope plus a 1000 lumen light-emitting diode (LED) cordless light source. Video recordings of porcine cystoscopy with a fiberoptic flexible cystoscope (Storz) were captured for each mobile device (iPhone 6, iPhone 6S, iPhone 7, Samsung S8, and Google Pixel) and for the high-definition (HD) H3-Z versatile camera setup with both the LED light source and the xenon light (XL) source. Eleven faculty urologists, blinded to the modality used, evaluated each video for image quality/resolution, brightness, color quality, sharpness, overall quality, and acceptability for diagnostic use. When comparing the Endockscope coupled to a Galaxy S8, iPhone 7, and iPhone 6S with the LED portable light source to the HD camera with XL, there were no statistically significant differences (p < 0.01) in any metric. Eighty-two percent and 55% of evaluators considered the iPhone 7 + LED light source and iPhone 6S + LED light, respectively, appropriate for diagnostic purposes as compared with 100% who considered both the HD camera with XL and Galaxy S8 + LED appropriate. The iPhone 6 and Google Pixel coupled with the LED source were both inferior to the HD camera with XL in all metrics. The Endockscope system with a LED light source ($45) when coupled with either an Apple iPhone 7 or Samsung Galaxy S8 is comparable to conventional videocystoscopy with a standard camera and XL light source (total cost: $45,000).