Mobile Camera Research Articles

Tourism Guide Application for Heritage Sites in Surabaya Based on Augmented Reality and GPS Gamification

One of the most important revenue streams for the nation's economy and development is tourism. The COVID-19 pandemic is, however, causing a reduction in tourism in Indonesia; Surabaya, the country's second-largest city, has seen a drop in visitors from 6.5 million to 500 thousand. To solve this problem, an innovation is required to boost the number of visitors to Indonesia's heritage sites who use contemporary technology, specifically smartphone with interactive media (with AR and GPS for instance). The author will develop an app that, depending on the user's location, will let them visit several Surabaya heritage sites and provide them with comprehensive details and instructions. Indonesia can stimulate its economy and return visitors to its heritage sites by employing this interactive gamification apps. However, on the examination to the user, we found a good implementation in detecting and measuring the nearest destination to the current user position, and how to parse into google map and guiding the destination to that heritage site. But we have some distraction when pointing the apps to the Augmented Reality (AR) marker on the site, since various historical places have various lighting, some devices are unable to take a clear picture of the marker due to its lack of lighting and abnormal size or form. Thus, it will be effective when the user points his smartphone camera to the AR marker, he/she supposed to use the smartphone lighting as well.

A comprehensive dataset of annotated oral cavity images for diagnosis of oral cancer and oral potentially malignant disorders

ObjectivesThis study aims to address the critical gap of unavailability of publicly accessible oral cavity image datasets for developing machine learning (ML) and artificial intelligence (AI) technologies for the diagnosis and prognosis of oral cancer (OCA) and oral potentially malignant disorders (OPMD), with a particular focus on the high prevalence and delayed diagnosis in Asia. Materials and methodsFollowing ethical approval and informed written consent, images of the oral cavity were obtained from mobile phone cameras and clinical data was extracted from hospital records from patients attending to the Dental Teaching Hospital, Peradeniya, Sri Lanka. After data management and hosting, image categorization and annotations were done by clinicians using a custom-made software tool developed by the research team. ResultsA dataset comprising 3000 high-quality, anonymized images obtained from 714 patients were classified into four distinct categories: healthy, benign, OPMD, and OCA. Images were annotated with polygonal shaped oral cavity and lesion boundaries. Each image is accompanied by patient metadata, including age, sex, diagnosis, and risk factor profiles such as smoking, alcohol, and betel chewing habits. ConclusionResearchers can utilize the annotated images in the COCO format, along with the patients’ metadata, to enhance ML and AI algorithm development.

Development of a deep-learning phenotyping tool for analyzing image-based strawberry phenotypes.

In strawberry farming, phenotypic traits (such as crown diameter, petiole length, plant height, flower, leaf, and fruit size) measurement is essential as it serves as a decision-making tool for plant monitoring and management. To date, strawberry plant phenotyping has relied on traditional approaches. In this study, an image-based Strawberry Phenotyping Tool (SPT) was developed using two deep-learning (DL) architectures, namely "YOLOv4" and "U-net" integrated into a single system. We aimed to create the most suitable DL-based tool with enhanced robustness to facilitate digital strawberry plant phenotyping directly at the natural scene or indirectly using captured and stored images. Our SPT was developed primarily through two steps (subsequently called versions) using image data with different backgrounds captured with simple smartphone cameras. The two versions (V1 and V2) were developed using the same DL networks but differed by the amount of image data and annotation method used during their development. For V1, 7,116 images were annotated using the single-target non-labeling method, whereas for V2, 7,850 images were annotated using the multitarget labeling method. The results of the held-out dataset revealed that the developed SPT facilitates strawberry phenotype measurements. By increasing the dataset size combined with multitarget labeling annotation, the detection accuracy of our system changed from 60.24% in V1 to 82.28% in V2. During the validation process, the system was evaluated using 70 images per phenotype and their corresponding actual values. The correlation coefficients and detection frequencies were higher for V2 than for V1, confirming the superiority of V2. Furthermore, an image-based regression model was developed to predict the fresh weight of strawberries based on the fruit size (R2 = 0.92). The results demonstrate the efficiency of our system in recognizing the aforementioned six strawberry phenotypic traits regardless of the complex scenario of the environment of the strawberry plant. This tool could help farmers and researchers make accurate and efficient decisions related to strawberry plant management, possibly causing increased productivity and yield potential.

An economical self-coalescing microfluidic device with an easily observable readout

Introduction: Self-coalescing microfluidic devices represent an exciting opportunity for leveraging viscous dominated flow phenomena to reconstitute a series of reagents with a single sample infusion. In a self-coalescing microfluidic device, spatially separated dried reagents are reconstituted using a single infusion. Due to the unique device geometry, the reagents remain spatially separated after infusion. As such, self-coalescing microfluidic devices have the potential to simplify point-of-care testing by simultaneously performing multiple colorimetric tests in one device. The current versions of these devices use standard, more costly microfabrication processes and are too small to be easily read by eye.Methods: Here, we created a low cost and scaled up version of a self-coalescing microfluidic device by using laser-cut-roll-based silicone tape. In addition to eliminating the need for cleanrooms, our approach simplifies the integration of assay reagents since they can be spotted onto a coverslip and covered with the tape microfluidic device. We empirically optimized our device, finding that flow rate significantly influenced the formation of self-coalescence as well as proper reagent reconstitution.Results and Discussion: When flow rates were too slow or fast, reagents would streak, leading to inadvertent mixing between different spatial locations. Our studies further revealed that geometry had a stronger influence on device operation in low-flow conditions. Our final optimized device exhibited a 100% success rate, demonstrated through self-coalescence with no reagent streaking at 100 μL/min, which enabled the isotropic diffusion of reagents. Furthermore, the spots are spatially separated and large enough to be visualized by the naked eye and captured by a smartphone camera for downstream analysis. Taken together, our innovative device leverages the advantages of sticker microfluidics and low-cost manufacturing methods to offer standalone functionality. This approach has the potential to significantly impact point-of-care diagnostics, particularly in resource-limited regions. By enabling multiplexed diagnostic assays through our novel technology, we aim to provide accessible and affordable healthcare solutions.

A 3D-Printed Do-It-Yourself ELISA Plate Reader as a Biosensor Tested on TNFα Assay.

Simple analytical devices suitable for the analysis of various biochemical and immunechemical markers are highly desirable and can provide laboratory diagnoses outside standard hospitals. This study focuses on constructing an easily reproducible do-it-yourself ELISA plate reader biosensor device, assembled from generally available and inexpensive parts. The colorimetric biosensor was based on standard 96-well microplates, 3D-printed parts, and a smartphone camera as a detector was utilized here as a tool to replace the ELISA method, and its function was illustrated in the assay of TNFα as a model immunochemical marker. The assay provided a limit of detection of 19 pg/mL when the B channel of the RGB color model was used for calibration. The assay was well correlated with the ELISA method, and no significant matrix effect was observed for standard biological samples or interference of proteins expected in a sample. The results of this study will inform the development of simple analytical devices easily reproducible by 3D printing and found on generally available electronics.

Observation of sunlight diffraction through tree twigs and leaves

This work reports the observation of sunlight diffraction through empty spaces in between twigs and leaves in tree crowns. The diffraction patterns are dominated by red wavelength on front followed by the trail of rest of spectral bands of white light rainbow bursts. Tree crowns act as obstacles and apertures causing diffraction/ interference of solar rays. Separation between leaves and twigs (d) is a few inches and distance between crown and camera (D) is in range of a few meters. Empty spaces in tree crown are much longer than red to violet wavelengths in sunlight therefore scattering, dispersion, reflection, diffraction and interference effects cannot be ruled out. Tree crown deflects solar rays into narrow angles all around to enable interference inside camera lens to create grating structure diffracting light into regular patterns. Diffraction pattern look like red petunia flower having 3 × 3 rose petals. Motorola mobile phone camera was used to record the sunlight diffraction patterns. Mobile camera consists of small focal length (≈5 mm) lens, a fixed opening aperture and an image sensor. Lens consists of few plastic or glass elements and sensor consists of charge coupled device (CCD) or CMOS technology. These diffraction/ interference patterns have potential to increase efficiency of Next Gen solar cells. This paper describes possible theoretical background of light diffraction and multiple ray interference through tree twigs and leaves.

Development of an Automated Free Flap Monitoring System Based on Artificial Intelligence

Meticulous postoperative flap monitoring is essential for preventing flap failure and achieving optimal results in free flap operations, for which physical examination has remained the criterion standard. Despite the high reliability of physical examination, the requirement of excessive use of clinician time has been considered a main drawback. To develop an automated free flap monitoring system using artificial intelligence (AI), minimizing human involvement while maintaining efficiency. In this prognostic study, the designed system involves a smartphone camera installed in a location with optimal flap visibility to capture photographs at regular intervals. The automated program identifies the flap area, checks for notable abnormalities in its appearance, and notifies medical staff if abnormalities are detected. Implementation requires 2 AI-based models: a segmentation model for automatic flap recognition in photographs and a grading model for evaluating the perfusion status of the identified flap. To develop this system, flap photographs captured for monitoring were collected from patients who underwent free flap-based reconstruction from March 1, 2020, to August 31, 2023. After the 2 models were developed, they were integrated to construct the system, which was applied in a clinical setting in November 2023. Conducting the developed automated AI-based flap monitoring system. Accuracy of the developed models and feasibility of clinical application of the system. Photographs were obtained from 305 patients (median age, 62 years [range, 8-86 years]; 178 [58.4%] were male). Based on 2068 photographs, the FS-net program (a customized model) was developed for flap segmentation, demonstrating a mean (SD) Dice similarity coefficient of 0.970 (0.001) with 5-fold cross-validation. For the flap grading system, 11 112 photographs from the 305 patients were used, encompassing 10 115 photographs with normal features and 997 with abnormal features. Tested on 5506 photographs, the DenseNet121 model demonstrated the highest performance with an area under the receiver operating characteristic curve of 0.960 (95% CI, 0.951-0.969). The sensitivity for detecting venous insufficiency was 97.5% and for arterial insufficiency was 92.8%. When applied to 10 patients, the system successfully conducted 143 automated monitoring sessions without significant issues. The findings of this study suggest that a novel automated system may enable efficient flap monitoring with minimal use of clinician time. It may be anticipated to serve as an effective surveillance tool for postoperative free flap monitoring. Further studies are required to verify its reliability.

PPG-based Vital Signs Measurement Using SmartPhone Camera: Review

Due to their growing popularity and rapid performance improvements, smartphones have the potential to be an accurate, low-cost physiological monitoring tool that is useful outside of the clinical setting. Since the cardiac pulse is responsible for the slight color changes in skin, a pulsatile signal that is also known as a photoplethysmographic (PPG) signal can be evaluated by utilizing a digital camera to record a video of your Region Of Interest (ROI). This review Study focuses on several research works that have used PPG-based video smartphone camera methods to measure Heart Rate (HR), Blood Pressure (BP), Oxygen Saturation (SpO2), and Respiratory Rate (RR). Comparing the approaches and results of each study (HR measurement studies produced acceptable findings with error rates less than 5%, BP error rates of results less than 10%, and SpO2 =<3.5%, by comparing the predicted models reading with the standard references reading) shows how smartphone-based physiological monitoring might be used in medical settings.

A smartphone application for personalized facial aesthetic monitoring.

Methods available at home for capturing facial images to track changes in skin quality and evaluate skincare treatments are limited. In this study, we developed a smartphone camera application (app) for personalized facial aesthetic monitoring. A face alignment indicators (FAIN) system utilizing facial landmark detection, an artificial intelligence technique, to estimate key facial parts, was implemented into the app to maintain a consistent facial appearance during image capture. The FAIN system is composed of a fixed target indicator and an alignment indicator that dynamically changes its shape according to the user's face position, size, and orientation. Users align their faces to match the alignment indicator with the fixed target indicator, and the image is automatically captured when alignment is achieved. We investigated the app's effectiveness in ensuring a consistent facial appearance by analyzing both geometric and colorimetric data. Geometric information from captured faces and colorimetric data from stickers applied to the faces were utilized. The coefficients of variation (CVs) for the L*, a*, and b* values of the stickers were higher compared to those measured by a colorimeter, with CVs of 14.9 times, 8.14 times, and 4.41 times for L*, a*, and b*, respectively. To assess the feasibility of the app for facial aesthetic monitoring, we tracked changes in pseudo-skin color on the cheek of a participant using skin-colored stickers. As a result, we observed the smallest color difference ∆Eab of 1.901, which can be considered as the experimentally validated detection limit using images acquired by the app. While the current monitoring method is a relative quantification approach, it contributes to evidence-based evaluations of skincare treatments.

Assessment of Image Quality and Child Acceptance for DSLR, Bridge and Smartphone Cameras Used in Dental Photography (A Comparative Study)

Digital photography is of huge importance during the course of dental treatments. It’s indispensable for recording data, patient motivation, communication with the lab etc. Many photography equipment are available and used in dentistry. Using digital photography is obtaining a footstep in pediatric dentistry. The purpose of this study is to evaluate the quality of dental images made by different digital photography mediums commonly used in dental clinics and measure the acceptance of children towards dental photography. Materials and Methods: in this study images produced by Three DSLR (digital single lens reflex) cameras along their prober lenses, one bridge camera and two smartphones with different sensor sizes were evaluated, based upon the sharpness, quality, color fidelity, and general context of the photographs. The participants were divided into three main groups: under-graduate students; graduated dentists and graduated dentists with photography experience. 20 pediatric patients were imaged using the aforementioned equipment. Results: DSLRs with macro lenses and ring flashes scored the highest means with statistically significant differences (p<0.05). From 20 pediatric patient photographed by each equipment used in this study 18 child accepted smartphone photography Conclusion: Older DSLRS are still usable for dental photography. Kit lenses or extension tubes with camera built in flashes are not appropriate for dental photography. Bridge cameras and smartphones are not adequate for dental photography.

Perbandingan Penegakan Aturan Lalu Lintas Antara Penerapan Tilang Manual Dan Electronic Traffic Law Enforcement (ETLE) Di Kota Padang

Enforcement of traffic rules is regulated in Law Number 22 of 2009 concerning Road Traffic and Transportation and Government Regulation No. 80 of 2012 concerning Procedures for Inspecting Motorized Vehicles on the Road and Enforcement of Traffic and Road Transportation Violations. The development of an electronic-based law enforcement system gave birth to Electronic Traffic Law Enforcement as an innovation to overcome obstacles in the application of manual fines. However, in its application, ETLE was also not free from various problems, so the urgency was born to conduct research to find out and compare the procedures for applying manual and ETLE tickets and how to apply future tickets in Indonesia, especially in the city of Padang as a research location. This study uses empirical juridical methods, the data used is primary data from interviews with Padang Police TMC operators and secondary data is in the form of document studies. The result of the research is that manual poles are directly imposed on violators by officers. Meanwhile, ETLE is a modern ticket system used by the police to monitor and prosecute traffic violations electronically with camera and sensor technology to identify vehicles that violate traffic rules, such as violating a red light or crossing the speed limit. Obstacles that occur in manual ticketing are low legal awareness from both law enforcers and the public. The obstacles that occur in ETLE are the limitations of static and mobile camera devices so that the application is not comprehensive. The application of fines in the future will still use a combination of manual and electronic ticketing due to the low level of public legal awareness and the uneven distribution of electronic ticketing devices in the city of Padang.

Pengolahan Citra Berbasis Video Proccesing dengan Metode Frame Difference untuk Deteksi Gerak

This study discusses the detection of motion of objects in video by utilizing the Frame difference method which aims to process video so as to produce Frames on moving objects. The use of mobile cameras produces video data that is used as test data, the test data is processed with the Frame difference method so as to produce a number of Frames on moving objects in order to detect moving objects in the video because the function of this method is a form of video background reduction that is simplified by a number of pixels in the video. This method process is based on the difference between two consecutive frames in the video aimed at finding differences that occur during the detection process. When processed for detection, the absolute value in the pixel is greater than the predetermined threshold value, it will be considered as a moving object, so that the detection results from the motion detection process will form a box object on the moving object. In this study, the test data used used 20 video data samples with descriptions, 10 test data with bright quality (daytime) and 10 unlit test data (night) with the aim of being able to see how much the level of performance accuracy of the Frame difference method. The test results obtained 16 out of 20 test data that were successfully detected correctly (True Positive), there were 2 test data that resulted in a False Positive error, and 2 test data that resulted in a False Negative error. This shows that the Frame difference method can provide a fairly high level of accuracy in detecting moving objects in the video. The percentage level of accuracy with confussion matrix testing has a precission value of 88%, recal 88% and an accuracy value of 80%.

BT20 Comparison of colour accuracy of a DSLR camera and mobile phone cameras used for dermoscopy images: a pilot study

BT20 Comparison of colour accuracy of a DSLR camera and mobile phone cameras used for dermoscopy images: a pilot study

Assessment of the effectiveness of artificial intelligence-based oral screening solution in the diagnosis of dental calculus, stains, and dental caries (Logy AI oral screening solution)

Background: The objective of this study was to clinically evaluate the precision of Logy AI's oral screening solution. This innovative module, driven by artificial intelligence, operates seamlessly through WhatsApp and as a standalone smartphone application. It is designed to detect various dental issues such as stains, calculus, and caries, utilizing images captured by a smartphone camera. The accuracy of the module was assessed by comparing its diagnoses with those made by dental professionals. Methods: A prospective clinical study was conducted in Saveetha Dental College, a tertiary care hospital in the southern part of India with 325 patients. Smartphone images taken were sent to the Logy AI oral screening solution which predicted if the patient had any oral hygiene issues like stains, calculus, and caries. Patients were examined by a dentist with visual-tactile and orthopantomogram (OPG) examination and were documented. Both were compared. Results: The accuracy of the Logy AI oral screening solution for the detection of stains, calculus, and caries was comparable with the dentist's diagnosis. The accuracy was 85% for caries, 97% for stains, and 83% for calculus. The sensitivity was 88% for caries, 89% for stains, and 82% for calculus. Conclusions: The Logy AI oral screening module demonstrates potential as an efficient oral screening tool suitable for community-level deployment, particularly in remote regions lacking access to costly dental equipment and healthcare professionals. Its accuracy and efficiency make it well-suited for operation in low-resource settings. Moreover, it holds promise as a valuable home screening tool for individuals seeking to monitor their oral health proactively.

Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation.

Diabetes has emerged as a worldwide health crisis, affecting approximately 537 million adults. Maintaining blood glucose requires careful observation of diet, physical activity, and adherence to medications if necessary. Diet monitoring historically involves keeping food diaries; however, this process can be labor-intensive, and recollection of food items may introduce errors. Automated technologies such as food image recognition systems (FIRS) can make use of computer vision and mobile cameras to reduce the burden of keeping diaries and improve diet tracking. These tools provide various levels of diet analysis, and some offer further suggestions for improving the nutritional quality of meals. The current study is a systematic review of mobile computer vision-based approaches for food classification, volume estimation, and nutrient estimation. Relevant articles published over the last two decades are evaluated, and both future directions and issues related to FIRS are explored.

A green chemical analysis of ethanol using a smart phone

This research presents a novel method for measuring ethanol concentrations using a smartphone. The method involves an oxidation reaction with potassium dichromate and concentrated sulfuric acid, resulting in a green-blue color formation. The color intensity, corresponding to ethanol concentrations ranging from 0 to 100%, was captured using a smartphone camera within a specialized photography box. The images were then analyzed using a specific application, converting the color signal into an absorbance value. The calibration curve demonstrated excellent linearity in the range of 0-0.55 v/v % and its detection limit is 0.01 v/v%, with a correlation coefficient exceeding 0.995. The method was successfully applied to measure ethanol in real samples, including ordinary rose water and a bitter wheat drink.•The method is inexpensive.•The method is rapid.•The method is green.

Development of Luminescent Biosensors for Calprotectin.

Calprotectin, a metal ion-binding protein complex, plays a crucial role in the innate immune system and has gained prominence as a biomarker for various intestinal and systemic inflammatory and infectious diseases, including inflammatory bowel disease (IBD) and tuberculosis (TB). Current clinical testing methods rely on enzyme-linked immunosorbent assays (ELISAs), limiting accessibility and convenience. In this study, we introduce the Fab-Enabled Split-luciferase Calprotectin Assay (FESCA), a novel quantitative method for calprotectin measurement. FESCA utilizes two new fragment antigen binding proteins (Fabs), CP16 and CP17, that bind to different epitopes of the calprotectin complex. These Fabs are fused with split NanoLuc luciferase fragments, enabling the reconstitution of active luciferase upon binding to calprotectin either in solution or in varied immobilized assay formats. FESCA's output luminescence can be measured with standard laboratory equipment as well as consumer-grade cell phone cameras. FESCA can detect physiologically relevant calprotectin levels across various sample types, including serum, plasma, and whole blood. Notably, FESCA can detect abnormally elevated native calprotectin from TB patients. In summary, FESCA presents a convenient, low-cost, and quantitative method for assessing calprotectin levels in various biological samples, with the potential to improve the diagnosis and monitoring of inflammatory diseases, especially in at-home or point-of-care settings.

Applicability of Digital Image Photogrammetry to Rapid Displacement Measurements of Structures in Restricted-Access and Controlled Areas: Case Study in Korea

Critical facilities are generally located in areas with restricted or controlled access, making it difficult for experts to monitor the structural health of enclosed infrastructures. Hence, a case study was conducted in South Korea to evaluate the applicability of digital image photogrammetry using commercial imaging devices in order to quickly measure the structural deformations of infrastructures in such areas. The applicability evaluation involved measuring the displacement of mechanically stabilized earth (MSE) walls. In the experiment, the displacement of MSE walls was first measured using the traditional monitoring method and the results were compared with those obtained via digital image photogrammetry using commercial imaging devices such as digital cameras and cellphones. The measurement results obtained with the cellphone camera had a maximum error of approximately 20 mm when compared with the results of the traditional monitoring method. Because this is a significant error, even when considering the mechanical error in the traditional monitoring method’s result, it was determined that monitoring using a cellphone camera is infeasible. However, the experimental results of digital image photogrammetry using a digital camera showed a maximum error of approximately 9 mm. Although this is a sizable error, the method was assessed to be technically feasible.

A Dataset for Kinship Estimation from Image of Hand Using Machine Learning

Kinship (Familial relationships) detection is crucial in many fields and has applications in biometric security, adoption, forensic investigations, and more. It is also essential during wars and natural disasters like earthquakes since it may aid in reunion, missing person searches, establishing emergency contacts, and providing psychological support. The most common method of determining kinship is DNA analysis which is highly accurate. Another approach, which is noninvasive, uses facial photos with computer vision and machine learning algorithms for kinship estimation. Each part of the Human -body has its own embedded information that can be extracted and adopted for identification, verification, or classification of that person. Kinship recognition is based on finding traits that are shared by every family. We investigate the use of hand geometry for kinship detection, which is a new approach. Because of the available hand image Datasets do not contain kinship ground truth; therefore, we created our own dataset. This paper describes the tools, methodology, and details of the collected MKH, which stands for the Mosul Kinship Hand, images dataset. The images of MKH dataset were collected using a mobile phone camera with a suitable setup and consisted of 648 images for 81 individuals from 14 families (8 hand situations per person). This paper also presents the use of this dataset in kinship prediction using machine learning. Google MdiaPipe was used for hand detection, segmentation, and geometrical key points finding. Handcraft feature extraction was used to extract 43 distinctive geometrical features from each image. A neural network classifier was designed and trained to predict kinship, yielding about 93% prediction accuracy. The results of this novel approach demonstrated that the hand possesses biometric characteristics that may be used to establish kinship, and that the suggested method is a promising way as a kinship indicator.

Rapid and portable quantification of HIV RNA via a smartphone-enabled digital CRISPR device and deep learning

For the 29.8 million people in the world living with HIV/AIDS and receiving antiretroviral therapy, it is crucial to monitor their HIV viral loads. To this end, rapid and portable diagnostic tools that can quantify HIV RNA are critically needed. We report herein a rapid and quantitative digital CRISPR-assisted HIV RNA detection assay that has been implemented within a portable smartphone-based device as a potential solution. Specifically, we first developed a fluorescence-based reverse transcription recombinase polymerase amplification (RT-RPA)-CRISPR assay that can efficiently detect HIV RNA at 42 °C. We then implemented this assay within a commercial stamp-sized digital chip, where RNA molecules were quantified as strongly fluorescent digital reaction wells. The isothermal reaction condition and the strong fluorescence in the digital chip simplified the design of thermal and optical modules, allowing us to engineer a palm-size device measuring 70 × 115 × 80 mm and weighing less than 0.6 kg. We also capitalized the smartphone by developing a custom app to control the device, perform the digital assay, and capture fluorescence images throughout the assay using the smartphone's camera. Moreover, we trained and verified a deep learning-based algorithm for analyzing fluorescence images and identifying positive digital reaction wells with high accuracy. Using our smartphone-enabled digital CRISPR device, we successfully detected as low as 75 copies of HIV RNA in just 15 min, showing its potential toward monitoring of HIV viral loads and aiding the global effort to combat the HIV/AIDS epidemic.