Smartphone Images Research Articles

Accurate Estimation of Body Weight of Pigs Through Smartphone Image Measurement App

Accurate Estimation of Body Weight of Pigs Through Smartphone Image Measurement App

Design and realization of a miniaturized high resolution computed tomography imaging spectrometer

The computed tomography imaging spectrometer (CTIS) is a relatively unknown snapshot hyperspectral camera. It utilizes computational imaging approaches to gain the hyperspectral image from a spatiospectral smeared sensor image. We present a strongly miniaturized system with a dimension of only 36mm x 40.5mm x 52.8mm and a diagonal field of view of 29°. We achieve this using a Galilean beam expander and a combination of off-the-shelf lenses, a highly aspherical imaging system from a commercial smartphone and a 13 MP monochrome smartphone image sensor. The reconstructed hyperspectral image has a spatial resolution of 400 x 300 pixel with 39 spectral channels.

Smartphone imaging spectrometer for egg/meat freshness monitoring.

Data transmission between spectroscopy equipment and mobile terminals is critical to realising hand-held field-level monitoring. Currently, on-the-go (OTG) communication technology is a convenient and efficient method of data transmission for mobile devices. However, few people associate spectroscopy equipment with smartphones through the OTG port. This study developed a portable imaging spectrometer with a spectral resolution of approximately 12 nm in the visible-near-infrared band (400-1000 nm). It can be connected to a smartphone through the USB-OTG port to process the spectral signal through the smartphone's system on a chip (SoC). It also displays real-time spectral images of the food samples through the smartphone's screen. Using a support vector machine (SVM) to classify the spectra of the various experimental samples (e.g. eggs and pork), the model prediction accuracy rate is approximately 90%. This further proves the reliability of the proposed smartphone imaging spectrometer for monitoring the freshness of food samples onsite.

Luminescent Europium Functionalized the Regular Octahedral Uio-66-Nh2 Enabling Fluorescent and Smartphone Imaging Detection of Tetracycline Residues Based on Antenna Effect and Inner Filter Effect

Luminescent Europium Functionalized the Regular Octahedral Uio-66-Nh2 Enabling Fluorescent and Smartphone Imaging Detection of Tetracycline Residues Based on Antenna Effect and Inner Filter Effect

Smartphone clip-on instrument and microfluidic processor for rapid sample-to-answer detection of Zika virus in whole blood using spatial RT-LAMP.

Rapid, simple, inexpensive, accurate, and sensitive point-of-care (POC) detection of viral pathogens in bodily fluids is a vital component of controlling the spread of infectious diseases. The predominant laboratory-based methods for sample processing and nucleic acid detection face limitations that prevent them from gaining wide adoption for POC applications in low-resource settings and self-testing scenarios. Here, we report the design and characterization of an integrated system for rapid sample-to-answer detection of a viral pathogen in a droplet of whole blood comprised of a 2-stage microfluidic cartridge for sample processing and nucleic acid amplification, and a clip-on detection instrument that interfaces with the image sensor of a smartphone. The cartridge is designed to release viral RNA from Zika virus in whole blood using chemical lysis, followed by mixing with the assay buffer for performing reverse-transcriptase loop-mediated isothermal amplification (RT-LAMP) reactions in six parallel microfluidic compartments. The battery-powered handheld detection instrument uniformly heats the compartments from below, and an array of LEDs illuminates from above, while the generation of fluorescent reporters in the compartments is kinetically monitored by collecting a series of smartphone images. We characterize the assay time and detection limits for detecting Zika RNA and gamma ray-deactivated Zika virus spiked into buffer and whole blood and compare the performance of the same assay when conducted in conventional PCR tubes. Our approach for kinetic monitoring of the fluorescence-generating process in the microfluidic compartments enables spatial analysis of early fluorescent "bloom" events for positive samples, in an approach called "Spatial LAMP" (S-LAMP). We show that S-LAMP image analysis reduces the time required to designate an assay as a positive test, compared to conventional analysis of the average fluorescent intensity of the entire compartment. S-LAMP enables the RT-LAMP process to be as short as 22 minutes, resulting in a total sample-to-answer time in the range of 17-32 minutes to distinguish positive from negative samples, while demonstrating a viral RNA detection as low as 2.70 × 102 copies per μl, and a gamma-irradiated virus of 103 virus particles in a single 12.5 μl droplet blood sample.

Reconstruction of a 3D Human Foot Shape Model Based on a Video Stream Using Photogrammetry and Deep Neural Networks

Reconstructed 3D foot models can be used for 3D printing and further manufacturing of individual orthopedic shoes, as well as in medical research and for online shoe shopping. This study presents a technique based on the approach and algorithms of photogrammetry. The presented technique was used to reconstruct a 3D model of the foot shape, including the lower arch, using smartphone images. The technique is based on modern computer vision and artificial intelligence algorithms designed for image processing, obtaining sparse and dense point clouds, depth maps, and a final 3D model. For the segmentation of foot images, the Mask R-CNN neural network was used, which was trained on foot data from a set of 40 people. The obtained accuracy was 97.88%. The result of the study was a high-quality reconstructed 3D model. The standard deviation of linear indicators in length and width was 0.95 mm, with an average creation time of 1 min 35 s recorded. Integration of this technique into the business models of orthopedic enterprises, Internet stores, and medical organizations will allow basic manufacturing and shoe-fitting services to be carried out and will help medical research to be performed via the Internet.

Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices.

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary, resulting in less accurate results. Recently, machine-learning (ML)-assisted models have been used in image analysis. We evaluated a combination of four ML models—logistic regression, support vector machine (SVM), random forest, and artificial neural network (ANN)—as well as three image color spaces, RGB, HSV, and LAB, for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras and users for food color and enzyme inhibition assays to create training and test datasets. The prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML models and color space combinations. All models better predicted coarse-level classifications than fine-grained concentration classes. ML models using the sample color along with a reference color increased the models’ ability to predict the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration class prediction accuracy obtained for food color was 0.966 when using the ANN model and LAB color space. The accuracy for enzyme inhibition assay was 0.908 when using the SVM model and LAB color space. Appropriate models and color space combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

Accuracy of Smartphone Images of the Cervix After Acetic Acid Application for Diagnosing Cervical Intraepithelial Neoplasia Grade 2 or Greater in Women With Positive Cervical Screening: A Systematic Review and Meta-Analysis.

PURPOSESmartphones are used in cervical screening for visual inspection after acetic acid or Lugol's iodine (VIA/VILI) application to capture and share images to improve the sensitivity and interobserver variability of VIA/VILI. We undertook a systematic review and meta-analysis assessing the diagnostic accuracy of smartphone images of the cervix at the time of VIA/VILI (termed S-VIA) in the detection of precancerous lesions in women undergoing cervical screening.METHODSThis systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies from January 1, 2010, to June 30, 2020, were assessed. MEDLINE/PubMed, Embase, CINAHL, Cochrane, and LILACS were searched. Cohort and cross-sectional studies were considered. S-VIA was compared with the reference standard of histopathology. We excluded studies where additional technology was added to the smartphone including artificial intelligence, enhanced visual assessment, and other algorithms to automatically diagnose precancerous lesions. The primary outcome was the accuracy of S-VIA for the diagnosis of cervical intraepithelial neoplasia grade 2 or greater (CIN 2+). Data were extracted, and we plotted the sensitivity, specificity, negative predictive value, and positive predictive value of S-VIA using forest plots. This study was prospectively registered with The International Prospective Register of Systematic Reviews:CRD42020204024.RESULTSSix thousand three studies were screened, 71 full texts assessed, and eight studies met criteria for inclusion, with six included in the final meta-analysis. The sensitivity of S-VIA for the diagnosis of CIN 2+ was 74.56% (95% CI, 70.16 to 78.95; I2 61.30%), specificity was 61.75% (95% CI, 56.35 to 67.15; I2 95.00%), negative predictive value was 93.71% (95% CI, 92.81 to 94.61; I2 0%), and positive predictive value was 26.97% (95% CI, 24.13 to 29.81; I2 61.3%).CONCLUSIONOur results suggest that S-VIA has accuracy in the detection of CIN 2+ and may provide additional support to health care providers delivering care in low-resource settings.

Use of Smartphones for the Detection of Uterine Cervical Cancer: A Systematic Review.

Simple SummarySeveral studies reveal that digital images taken with a smartphone after a visual inspection with acetic acid (VIA) or Lugol’s iodine (VILI) may be useful for detecting cervical intraepithelial neoplasia. Therefore, smartphones could be useful in the early detection of uterine cervical lesions and an alternative to colposcopy in countries with limited health resources. In this systematic review, we found that the VIA using a smartphone seems to be more sensitive than the VIA, VILI, or VIA/VILI examinations with the naked eye. Therefore, it can improve diagnostic accuracy for the detection of uterine cervical lesions.Little is known regarding the usefulness of the smartphone in the detection of uterine cervical lesions or uterine cervical cancer. Therefore, we evaluated the usefulness of the smartphone in the detection of uterine cervical lesions and measured its diagnostic accuracy by comparing its findings with histological findings. We conducted a systematic review to identify studies on the usefulness of the smartphone in detecting uterine cervical lesions indexed in SCOPUS, MEDLINE/PubMed, Cochrane, OVID, Web of Science, and SciELO until November 2020. The risk of bias and applicability was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A total of 16 studies that evaluated the usefulness of the smartphone in the detection of uterine cervical lesions based on the images clicked after visual inspection with acetic acid (VIA), Lugol’s iodine (VILI), or VIA/VILI combination were included in the study. Five studies estimated diagnostic sensitivity and specificity, nine described diagnostic concordance, and five described the usefulness of mobile technology. Among the five first studies, the sensitivity ranged between 66.7% (95% confidence interval (CI); 30.0–90.3%) and 94.1% (95% CI; 81.6–98.3%), and the specificity ranged between 24.0% (95% CI; 9.0–45.0%) and 85.7% (95% CI; 76.7–91.6%). The risk of bias was low (20%), and the applicability was high. In conclusion, the smartphone images clicked after a VIA were found to be more sensitive than those following the VILI method or the VIA/VILI combination and naked-eye techniques in detecting uterine cervical lesions. Thus, a smartphone may be useful in the detection of uterine cervical lesions; however, its sensitivity and specificity are still limited.

Improvements in Smartphone and Night Vision Imaging Technologies Enable Low Cost, On-Site Assays of Bioluminescent Cells.

Technologies enabling on-site environmental detection or medical diagnostics in resource-limited settings have a strong disruptive potential compared to current analytical approaches that require trained personnel in laboratories with immobile, resource intensive instrumentation. Handheld devices, such as smartphones, are now routinely produced with CPUs, RAM, wireless data transfer capabilities, and high-resolution complementary metal oxide semiconductor (CMOS) cameras capable of supporting the capture and processing of bioluminescent signals. In theory, combining the capabilities of these devices with continuously bioluminescent human cell-based bioreporters would allow them to replicate the functionality of more expensive, more complex, and less flexible platforms while supporting human-relevant conclusions. In this work, we compare the performance of smartphone (CMOS) and night vision (image intensifier) devices with in vivo (CCD camera), and in vitro (photomultiplier tube) laboratory instrumentation for monitoring signal dynamics from continuously bioluminescent human cellular models under toxic, stable, and induced expression scenarios. All systems detected bioluminescence from cells at common plating densities. While the in vivo and in vitro systems were more sensitive and detected signal dynamics representing cellular health changes earlier, the night vision and smartphone systems also detected these changes with relatively similar coefficients of variation and linear detection capabilities. The smartphone system did not detect transcriptional induction. The night vision system did detect transcriptional activation, but was less sensitive than the in vivo or in vitro systems and required a stronger induction before the change could be resolved.

Automatic Recognition of Ocular Surface Diseases on Smartphone Images Using Densely Connected Convolutional Networks.

Ocular surface disorder is one of common and prevalence eye diseases and complex to be recognized accurately. This work presents automatic classification of ocular surface disorders in accordance with densely connected convolutional networks and smartphone imaging. We use various smartphone cameras to collect clinical images that contain normal and abnormal, and modify end-to-end densely connected convolutional networks that use a hybrid unit to learn more diverse features, significantly reducing the network depth, the total number of parameters and the float calculation. The validation results demonstrate that our proposed method provides a promising and effective strategy to accurately screen ocular surface disorders. In particular, our deeply learned smartphone photographs based classification method achieved an average automatic recognition accuracy of 90.6%, while it is conveniently used by patients and integrated into smartphone applications for automatic patient-self screening ocular surface diseases without seeing a doctor in person in a hospital.

A triple-layer microchannel array chip with micro through-holes for smartphone based point-of-care testing of biomarker

Colorimetric detection on a microchip frequently suffers the sensitivity problem due to the limited optical path length. Herein, a point-of-care testing (POCT) device based on the colorimetric detection on a microchip with an array of micro through holes for enlarged optical length and an optical imaging system was demonstrated. Each micro through hole has a depth of 2 mm and a volume roughly 0.6 μl, connecting to a microchannel for the efficient reaction. Up to 32 micro through holes were arranged compactly along a round region with a diameter of 13 mm to make them effectively captured by a smartphone camera in a short distance (6.5 cm). For reliable smartphone imaging, a handheld, battery powered optical imaging cassette was made by three-dimensional (3D) printing. A data processing program was also written to realize the detection area selection, RGB (red, green, blue) value reading and detection signal calculation. With the integrated device, a biomarker, human immunodeficiency virus (HIV) p24 antigen in human serum was detected down to 20 pg/ml, much better than that with microchannels. The proposed microchip and the imaging system can be a promising POCT or on-site detection tool based on colorimetric detection.

Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms

PurposeNon-melanoma skin cancer (NMSC) is the most frequent keratinocyte-origin skin tumor. It is confirmed that dermoscopy of NMSC confers a diagnostic advantage as compared to visual face-to-face assessment. COVID-19 restrictions diagnostics by telemedicine photos, which are analogous to visual inspection, displaced part of in-person visits. This study evaluated by a dual convolutional neural network (CNN) performance metrics in dermoscopic (DI) versus smartphone-captured images (SI) and tested if artificial intelligence narrows the proclaimed gap in diagnostic accuracy.MethodsA CNN that receives a raw image and predicts malignancy, overlaid by a second independent CNN which processes a sonification (image-to-sound mapping) of the original image, were combined into a unified malignancy classifier. All images were histopathology-verified in a comparison between NMSC and benign skin lesions excised as suspected NMSCs. Study criteria outcomes were sensitivity and specificity for the unified output.ResultsImages acquired by DI (n = 132 NMSC, n = 33 benign) were compared to SI (n = 170 NMSC, n = 28 benign). DI and SI analysis metrics resulted in an area under the curve (AUC) of the receiver operator characteristic curve of 0.911 and 0.821, respectively. Accuracy was increased by DI (0.88; CI 81.9–92.4) as compared to SI (0.75; CI 68.1–80.6, p < 0.005). Sensitivity of DI was higher than SI (95.3%, CI 90.4–98.3 vs 75.3%, CI 68.1–81.6, p < 0.001), but not specificity (p = NS).ConclusionTelemedicine use of smartphone images might result in a substantial decrease in diagnostic performance as compared to dermoscopy, which needs to be considered by both healthcare providers and patients.

Lead Assays with Smartphone Detection Using a Monolithic Rod with 4-(2-Pyridylazo) Resorcinol.

A monolithic rod of polyurethane foam–[4-(2-pyridylazo) resorcinol] (PUF–PAR) as a simple chemical sensor for lead assays with smartphone detection and image processing was developed. With readily available simple apparatus such as a plastic cup and a stirrer rod, the monolithic PUF rod was synthesized in a glass tube. The monolithic PUF–PAR rod could be directly loaded by standard/sample solution without sample preparation. A one-shot image in G/B value from a profile plot in ImageJ for a sample with triplicate results via a single standard calibration approach was obtained. A linear single standard calibration was: [G/B value] = −0.038[µg Pb2+] + 2.827, R2 = 0.95 for 10–30 µg Pb2+ with a limit of quantitation (LOQ) of 33 µg L−1. The precision was lower than 15% RSD. The proposed method was tested by an assay for Pb2+ contents in drinking water samples from Bangkok. The results obtained by the proposed method agree with those of ICP-OES and with 100–120% recovery, demonstrating that the method is useful for screening on-site water monitoring.

Feasibility assessment of infectious keratitis depicted on slit-lamp and smartphone photographs using deep learning

BackgroundThis study aims to investigate how infectious keratitis depicted on slit-lamp and smartphone photographs can be reliably assessed using deep learning. Materials and MethodsWe retrospectively collected a dataset consisting of 5,673 slit-lamp photographs and 400 smartphone photographs acquired on different subjects. Based on multiple clinical tests (e.g., cornea scraping), these photographs were diagnosed and classified into four categories, including normal (i.e., no keratitis), bacterial keratitis (BK), fungal keratitis (FK), and herpes simplex virus stromal keratitis (HSK). We preprocessed these slit-lamp images into two separate subgroups: (1) global images and (2) regional images. The cases in each group were randomly split into training, internal validation, and independent testing sets. Then, we implemented a deep learning network based on the InceptionV3 by fine-tuning its architecture and used the developed network to classify these slit-lamp images. Additionally, we investigated the performance of the InceptionV3 model in classifying infectious keratitis depicted on smartphone images. We, in particular, clarified whether the computer model trained on the global images outperformed the one trained on the regional images. The quadratic weighted kappa (QWK) and the receiver operating characteristic (ROC) analysis were used to assess the performance of the developed models. ResultsOur experiments on the independent testing sets showed that the developed models achieved the QWK of 0.9130 (95% CI: 88.99–93.61%) and 0.8872 (95% CI: 86.13–91.31%), and 0.5379 (95% CI, 48.89–58.69%) for the global images, the regional images, and the smartphone images, respectively. The area under the ROC curves (AUCs) were 0.9588 (95% CI: 94.28–97.48%), 0.9425 (95% CI: 92.35–96.15%), and 0.8529 (95% CI: 81.79–88.79%) for the same test sets, respectively. ConclusionThe deep learning solution demonstrated very promising performance in assessing infectious keratitis depicted on slit-lamp photographs and the images acquired by smartphones. In particular, the model trained on the global images outperformed that trained on the regional images.

Semantic VPS for Smartphone Localization in Challenging Urban Environments.

Accurate smartphone-based outdoor localization systems in deep urban canyons are increasingly needed for various IoT applications. As smart cities have developed, building information modeling (BIM) has become widely available. This article, for the first time, presents a semantic Visual Positioning System (VPS) for accurate and robust position estimation in urban canyons where the global navigation satellite system (GNSS) tends to fail. In the offline stage, a material segmented BIM is used to generate segmented images. In the online stage, an image is taken with a smartphone camera that provides textual information about the surrounding environment. The approach utilizes computer vision algorithms to segment between the different types of material class identified in the smartphone image. A semantic VPS method is then used to match the segmented generated images with the segmented smartphone image. Each generated image contains position information in terms of latitude, longitude, altitude, yaw, pitch, and roll. The candidate with the maximum likelihood is regarded as the precise position of the user. The positioning result achieved an accuracy of 2.0 m among high-rise buildings on a street, 5.5 m in a dense foliage environment, and 15.7 m in an alleyway. This represents an improvement in positioning of 45% compared to the current state-of-the-art method. The estimation of yaw achieved accuracy of 2.3°, an eight-fold improvement compared to the smartphone IMU.

Your mileage may vary: impact of data input method for a deep learning bone age app’s predictions

The purpose of this study was to evaluate agreement in predictions made by a bone age prediction application ("app") among three data input methods. The 16Bit Bone Age app is a browser-based deep learning application for predicting bone age on pediatric hand radiographs; recommended data input methods are direct image file upload or smartphone-capture of image. We collected 50 hand radiographs, split equally among 5 bone age groups. Three observers used the 16Bit Bone Age app to assess these images using 3 different data input methods: (1) direct image upload, (2) smartphone photo of image in radiology reading room, and (3) smartphone photo of image in a clinic. Interobserver agreement was excellent for direct upload (ICC = 1.00) and for photos in reading room (ICC = 0.96) and good for photos in clinic (ICC = 0.82), respectively. Intraobserver agreement for the entire test set across the 3 data input methods was variable with ICCs of 0.95, 0.96, and 0.57 for the 3 observers, respectively. Our findings indicate that different data input methods can result in discordant bone age predictions from the 16Bit Bone Age app. Further study is needed to determine the impact of data input methods, such as smartphone image capture, on deep learning app performance and accuracy.

Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing.

Grain moisture content (GMC) is a key indicator of the appropriate harvest period of rice. Conventional testing is time-consuming and laborious, thus not to be implemented over vast areas and to enable the estimation of future changes for revealing optimal harvesting. Images of single panicles were shot with smartphones and corrected using a spectral–geometric correction board. In total, 86 panicle samples were obtained each time and then dried at 80 °C for 7 days to acquire the wet-basis GMC. In total, 517 valid samples were obtained, in which 80% was randomly used for training and 20% was used for testing to construct the image-based GMC assessment model. In total, 17 GMC surveys from a total of 201 samples were also performed from an area of 1 m2 representing on-site GMC, which enabled a multi-day GMC prediction. Eight color indices were selected using principal component analysis for building four machine learning models, including random forest, multilayer perceptron, support vector regression (SVR), and multivariate linear regression. The SVR model with a MAE of 1.23% was the most suitable for GMC of less than 40%. This study provides a real-time and cost-effective non-destructive GMC measurement using smartphones that enables on-farm prediction of harvest dates and facilitates the harvesting scheduling of agricultural machinery.

EXPERIMENTS USING SMARTPHONE-BASED VIDEOGRAMMETRY FOR LOW-COST CULTURAL HERITAGE DOCUMENTATION

Abstract. The rapid development of 3D scanning technology is a welcome progress in the field of tangible cultural heritage documentation. While active sensors such as handheld Time-of-Flight (ToF) cameras and lidar have recently generated much hype, developments in low-cost imaging sensors have also seen long strides in recent decades. This paper aims to see the potential of videogrammetry for the purposes of heritage documentation. This technique has existed for decades, but we argue that when combined with modern smartphone sensors and proper photogrammetric processing workflow it may present an interesting low-cost solution for 3D scanning. Furthermore, the paper wishes to address the requirement for a certain geometric quality in heritage documentation and how the proposed method may fulfil them. For this reason, comparisons between the videogrammetric result and traditional DSLR close range photogrammetry will be described to determine its suitability for heritage documentation. Results show that using modern low-cost smartphone imaging sensors, a good compromise between geometric quality and overall cost in the context of cultural heritage recording is possible to achieve.

A lab-on-a-disc platform based on nickel nanowire net and smartphone imaging for rapid and automatic detection of foodborne bacteria

Foodborne pathogenic bacteria have been considered as a major risk factor for food safety. It is of great significance to carry out in-field screening of pathogenic bacteria to prevent the outbreaks of foodborne diseases. In this study, a portable lab-on-a-disc platform with a microfluidic disc was developed for rapid and automatic detection of Salmonella typhimurium using a nickel nanowire (NiNW) net for effective separation of target bacteria, horseradish peroxidase nanoflowers (HRP NFs) for efficient amplification of biological signals, and a self-developed smartphone APP for accurate analysis of colorimetric images. First, the microfluidic disc was preloaded with reagents and samples and centrifuged to form one bacterial sample column, one immune NiNW column, one HRP NF column, two washing buffer columns and one tetramethylbenzidine (TMB) column, which were separated by air gaps. Then, a rotatable magnetic field was specifically developed to assemble the NiNWs into a net, which was automatically controlled by a stepped motor to successively pass through the sample column for specific capture of target bacteria, the HRP NF column for specific label of target bacteria, the washing columns for effective removal of sample background and non-specific binding NFs, and the TMB column for colorimetric determination of target bacteria. The color change of TMB from colorless to blue was finally analyzed using the smartphone APP to quantitatively determine the target bacteria. This lab-on-a-disc platform could detect Salmonella typhimurium from 5.6 × 101 CFU/20 µL to 5.6 × 105 CFU/20 µL in 1 h with a lower detection limit of 56 CFU/20 µL. The recovery of target bacteria in spiked chicken samples ranged from 97.5% to 101.8%. This portable platform integrating separation, labeling, washing, catalysis and detection onto a single disc is featured with automatic operation, fast reaction, and small size and has shown its potential for in-field detection of foodborne pathogens.