Search

Deep learning-enabled smartphone-based image processing has significant advantages in the development of point-of-care diagnostics. Conventionally, most deep-learning applications require task specific large scale expertly annotated datasets. Therefore, these algorithms are oftentimes limited only to applications that have large retrospective datasets available for network development. Here, we report the possibility of utilizing adversarial neural networks to overcome this challenge by expanding the utility of non-specific data for the development of deep learning models. As a clinical model, we report the detection of fentanyl, a small molecular weight drug that is a type of opioid, at the point-of-care using a deep-learning empowered smartphone assay. We used the catalytic property of platinum nanoparticles (PtNPs) in a smartphone-enabled microchip bubbling assay to achieve high analytical sensitivity (detecting fentanyl at concentrations as low as 0.23 ng mL

Deep learning neural networks have been used to predict the developmental fate and implantation potential of embryos with high accuracy. Such networks have been used as an assistive quality assurance (QA) tool to identify perturbations in the embryo culture environment which may impact clinical outcomes. The present study aimed to evaluate the utility of an AI-QA tool to consistently monitor ART staff performance (MD and embryologist) in embryo transfer (ET), embryo vitrification (EV), embryo warming (EW), and trophectoderm biopsy (TBx).Pregnancy outcomes from groups of 20 consecutive elective single day 5 blastocyst transfers were evaluated for the following procedures: MD performed ET (N = 160 transfers), embryologist performed ET (N = 160 transfers), embryologist performed EV (N = 160 vitrification procedures), embryologist performed EW (N = 160 warming procedures), and embryologist performed TBx (N = 120 biopsies). AI-generated implantation probabilities for the same embryo cohorts were estimated, as were mean AI-predicted and actual implantation rates for each provider and compared using Wilcoxon singed-rank test.Actual implantation rates following ET performed by one MD provider: "H" was significantly lower than AI-predicted (20% vs. 61%, p = 0.001). Similar results were observed for one embryologist, "H" (30% vs. 60%, p = 0.011). Embryos thawed by embryologist "H" had lower implantation rates compared to AI prediction (25% vs. 60%, p = 0.004). There were no significant differences between actual and AI-predicted implantation rates for EV, TBx, or for the rest of the clinical staff performing ET or EW.AI-based QA tools could provide accurate, reproducible, and efficient staff performance monitoring in an ART practice.

PURPOSE: A deep learning artificial intelligence (AI) algorithm has been demonstrated to outperform embryologists in identifying euploid embryos destined to implant with an accuracy of 75.3% (1). Our aim was to evaluate the performance of highly trained embryologists in selecting top quality day 5 euploid blastocysts with and without the aid of a deep learning algorithm. MATERIALS AND METHODS: A non-overlapping series of 200 sets of day 5 euploid embryo images with known implantation outcomes was distributed to 17 highly trained embryologists. One embryo in each set was known to have implanted and one failed implantation. They were asked to select which embryo to transfer from each set. The same 200 sets of embryos, with indication of which embryo in each set had been identified by the algorithm as more likely to implant was then distributed. Chi-squared, t-test, and receiver operating curves were performed to compare the embryologist performeance with and without AI. RESULTS: Fourteen embryologists completed both assessments. Embryologists provided with AI results selected successfully implanted embryos in 73.6% of cases compared to 65.5% for those selected using visual assessments alone (p

In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network’s performance can degrade substantially when applied to a dataset with a different distribution. Here, we show that adversarial learning can be used to develop high-performing networks trained on unannotated medical images of varying image quality. Specifically, we used low-quality images acquired using inexpensive portable optical systems to train networks for the evaluation of human embryos, the quantification of human sperm morphology and the diagnosis of malarial infections in the blood, and show that the networks performed well across different data distributions. We also show that adversarial learning can be used with unlabelled data from unseen domain-shifted datasets to adapt pretrained supervised networks to new distributions, even when data from the original distribution are not available. Adaptive adversarial networks may expand the use of validated neural-network models for the evaluation of data collected from multiple imaging systems of varying quality without compromising the knowledge stored in the network.

To determine whether convolutional neural networks (CNN) can be used to accurately ascertain the patient identity (ID) of cleavage and blastocyst stage embryos based on image data alone.A CNN model was trained and validated over three replicates on a retrospective cohort of 4889 time-lapse embryo images. The algorithm processed embryo images for each patient and produced a unique identification key that was associated with the patient ID at a timepoint on day 3 (~ 65 hours post-insemination (hpi)) and day 5 (~ 105 hpi) forming our data library. When the algorithm evaluated embryos at a later timepoint on day 3 (~ 70 hpi) and day 5 (~ 110 hpi), it generates another key that was matched with the patient's unique key available in the library. This approach was tested using 400 patient embryo cohorts on day 3 and day 5 and number of correct embryo identifications with the CNN algorithm was measured.CNN technology matched the patient identification within random pools of 8 patient embryo cohorts on day 3 with 100% accuracy (n = 400 patients; 3 replicates). For day 5 embryo cohorts, the accuracy within random pools of 8 patients was 100% (n = 400 patients; 3 replicates).This study describes an artificial intelligence-based approach for embryo identification. This technology offers a robust witnessing step based on unique morphological features of each embryo. This technology can be integrated with existing imaging systems and laboratory protocols to improve specimen tracking.

To determine if deep learning artificial intelligence algorithms can be used to accurately identify key morphologic landmarks on oocytes and cleavage stage embryo images for micromanipulation procedures such as intracytoplasmic sperm injection (ICSI) or assisted hatching (AH).Two convolutional neural network (CNN) models were trained, validated, and tested over three replicates to identify key morphologic landmarks used to guide embryologists when performing micromanipulation procedures. The first model (CNN-ICSI) was trained (n = 13,992), validated (n = 1920), and tested (n = 3900) to identify the optimal location for ICSI through polar body identification. The second model (CNN-AH) was trained (n = 13,908), validated (n = 1908), and tested (n = 3888) to identify the optimal location for AH on the zona pellucida that maximizes distance from healthy blastomeres.The CNN-ICSI model accurately identified the polar body and corresponding optimal ICSI location with 98.9% accuracy (95% CI 98.5-99.2%) with a receiver operator characteristic (ROC) with micro and macro area under the curves (AUC) of 1. The CNN-AH model accurately identified the optimal AH location with 99.41% accuracy (95% CI 99.11-99.62%) with a ROC with micro and macro AUCs of 1.Deep CNN models demonstrate powerful potential in accurately identifying key landmarks on oocytes and cleavage stage embryos for micromanipulation. These findings are novel, essential stepping stones in the automation of micromanipulation procedures.

Assessment of swine semen quality is important as it is used as an estimate of the fertility of an ejaculate. There are many methods to measure sperm morphology, concentration, and motility, however, some methods require expensive instrumentation or are not easy to use on-farm. A portable, low-cost, automated device could provide the potential to assess semen quality in field conditions. The objective of this study was to validate the use of Fertile-Eyez (FE), a smartphone-based device, to measure sperm concentration, total motility, and morphology in boar ejaculates. Semen from six sexually mature boars were collected and mixed to create a total of 18 unique semen samples for system evaluations. Each sample was then diluted to 1:4, 1:8, 1:10, and 1:16 (for concentration only) with Androhep Plus semen extender (n = 82 total). Sperm concentration was evaluated using FE and compared to results measured using a Nucleocounter and computer assisted sperm analysis (CASA: Ceros II, Hamilton Thorne). Sperm motility was evaluated using FE and CASA. Sperm morphological assessments were evaluated by a single technician manually counting abnormalities and compared to FE deep-learning technology. Data were analyzed using both descriptive statistics (mean, standard deviation, intra-assay coefficient of variance, and residual standard deviation [RSD]) and statistical tests (correlation analysis between devices and Bland-Altman methods). Concentration analysis was strongly correlated (n = 18; r > 0.967; P < 0.0001) among all devices and dilutions. Analysis of motility showed moderate correlation and was significant when all dilutions are analyzed together (n = 54; r = 0.558; P < 0.001). The regression analysis for motility also showed the RSD as 3.95% between FE and CASA indicating a tight fit between devices. This RSD indicates that FE can find boars with unacceptable motility (boars for example with less than 70%) which impact fertility and litter size. The Bland-Altman analysis showed that FE-estimated morphological assessment and the conventionally estimated morphological score were similar, with a mean difference of ~1% (%95 Limits of Agreement: −6.2 to 8.1; n = 17). The results of this experiment demonstrate that FE, a portable and automated smartphone-based device, is capable of assessing concentration, motility, and morphology of boar semen samples.

Study question Can convolutional neural networks (CNN) be used as a witnessing system to accurately track and identify patient specific embryos at the cleavage stage of development? Summary answer We developed the first artificial intelligence driven witnessing system to accurately track cleavage and blastocyst stage embryos in a human ART laboratory. What is known already There are reports of human errors in embryo tracking that have led to the births of children with different genetic makeup than their birth parents. Clinical practices rely on manual identification, barcodes or radio-frequency identification technology to track embryos. These systems are designed to track culture dishes but are unable to monitor developing embryos within the dish to help ensure an error-free patient match. Previously, we developed an AI witnessing system to track blastocysts with 100% accuracy. The goal of this study was to determine whether an AI witnessing system could be developed that accurately tracks cleavage stage embryos. Study design, size, duration A pre-developed deep neural network technology was first trained and tested on 4944 embryos images. The algorithm processed embryo images for each patient and produced a unique key that was associated with the patient ID at 60 hpi, which formed our library. When the algorithm evaluated embryos at 64 hpi it generated another key that was matched with the patient’s unique key available in the library. Participants/materials, setting, methods A total of 3068 embryos from 412 patients were examined by the CNN at both 60 hpi and 64 hpi. These timepoints were chosen as they reflect the time our laboratory evaluates Day 3 embryos (60 hpi) and the time we move them to another dish and prepare them for transfer (64 hpi). The patient cohorts ranged from 3-12 embryos per patient. Main results and the role of chance The accuracy of the CNN in correctly matching the patient identification with the patient embryo cohort was 100% (CI: 99.1% to 100.0%, n = 412). Limitations, reasons for caution Limitations of this study include that all embryos were imaged under identical conditions and within the same EmbryoScope. Additionally, this study only examined fresh Day 3 embryos cultured over a span of 4 hours. Future studies should include images of fresh and frozen/thawed embryos captured using different imaging systems. Wider implications of the findings This study describes the first artificial intelligence-based approach for cleavage stage embryo tracking and patient specimen identification in the IVF laboratory. This technology offers a robust witnessing step based on unique morphological features that are specific to each individual embryo. Trial registration number This work was partially supported by the Brigham Precision Medicine Developmental Award (Brigham Precision Medicine Program, Brigham and Women’s Hospital), Partners Innovation Discovery Grant (Partners Healthcare), and R01AI118502, and R01AI138800.

CRISPR (Clustered regularly interspaced short palindromic repeats)‐based diagnostic technologies have emerged as a promising alternative to accelerate delivery of SARS‐CoV‐2 molecular detection at the point of need. However, efficient translation of CRISPR‐diagnostic technologies to field application is still hampered by dependence on target amplification and by reliance on fluorescence‐based results readout. Herein, an amplification‐free CRISPR/Cas12a‐based diagnostic technology for SARS‐CoV‐2 RNA detection is presented using a smartphone camera for results readout. This method, termed Cellphone‐based amplification‐free system with CRISPR/CAS‐dependent enzymatic (CASCADE) assay, relies on mobile phone imaging of a catalase‐generated gas bubble signal within a microfluidic channel and does not require any external hardware optical attachments. Upon specific detection of a SARS‐CoV‐2 reverse‐transcribed DNA/RNA heteroduplex target (orf1ab) by the ribonucleoprotein complex, the transcleavage collateral activity of the Cas12a protein on a Catalase:ssDNA probe triggers the bubble signal on the system. High analytical sensitivity in signal detection without previous target amplification (down to 50 copies µL−1) is observed in spiked samples, in ≈71 min from sample input to results readout. With the aid of a smartphone vision tool, high accuracy (AUC = 1.0; CI: 0.715 – 1.00) is achieved when the CASCADE system is tested with nasopharyngeal swab samples of PCR‐positive COVID‐19 patients., The CASCADE (Cellphone‐Based Amplification‐Free System with CRISPR/CAS‐Dependent Enzymatic) assay relies on the Cas12‐mediated trans‐cleavage of a Catalase:ssDNA probe, upon recognition of a specific nucleic acid target, such as SARS‐CoV‐2 genomic RNA. This generates a gas bubble signal within a microfluidic channel which is then detected using a smartphone camera and a dedicated smartphone application, without any optical hardware attachment.

Embryo assessment and selection is a critical step in an In-vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopic analysis done by embryologists or semi-automated time-lapse imaging systems are highly subjective, time-consuming, or expensive. Availability of cost-effective and easy-to-use hardware and software for embryo image data acquisition and analysis can significantly empower embryologists towards more efficient clinical decisions both in resource-limited and resource-rich settings. Here, we report the development of two inexpensive (90% accuracy.

A virus detection method using deep learning–based analysis of smartphone-recorded microchip images without any optical hardware., Emerging and reemerging infections present an ever-increasing challenge to global health. Here, we report a nanoparticle-enabled smartphone (NES) system for rapid and sensitive virus detection. The virus is captured on a microchip and labeled with specifically designed platinum nanoprobes to induce gas bubble formation in the presence of hydrogen peroxide. The formed bubbles are controlled to make distinct visual patterns, allowing simple and sensitive virus detection using a convolutional neural network (CNN)–enabled smartphone system and without using any optical hardware smartphone attachment. We evaluated the developed CNN-NES for testing viruses such as hepatitis B virus (HBV), HCV, and Zika virus (ZIKV). The CNN-NES was tested with 134 ZIKV- and HBV-spiked and ZIKV- and HCV-infected patient plasma/serum samples. The sensitivity of the system in qualitatively detecting viral-infected samples with a clinically relevant virus concentration threshold of 250 copies/ml was 98.97% with a confidence interval of 94.39 to 99.97%.

Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.

Staff competency is a crucial component of the in vitro fertilization (IVF) laboratory quality management system because it impacts clinical outcomes and informs the key performance indicators (KPIs) used to continuously monitor and assess culture conditions. Contemporary quality control and assurance in the IVF lab can be automated (collect, store, retrieve, and analyze), to elevate quality control and assurance beyond the cursory monthly review. Here we demonstrate that statistical KPI monitoring systems for individual embryologist performance and culture conditions can be detected by artificial intelligence systems to provide systemic, early detection of adverse outcomes, and identify clinically relevant shifts in pregnancy rates, providing critical validation for two statistical process controls proposed in the Vienna Consensus Document; intracytoplasmic sperm injection (ICSI) fertilization rate and day 3 embryo quality.

A critical factor that influences the success of an in-vitro fertilization (IVF) treatment cycle is the quality of the transferred embryo. Embryo morphology assessments, conventionally performed through manual microscopic analysis suffer from disparities in practice, selection criteria, and subjectivity due to the experience of the embryologist. Convolutional neural networks (CNNs) are powerful, promising algorithms with significant potential for accurate classifications across many object categories. Network architectures and hyper-parameters affect the efficiency of CNNs for any given task. Here, we evaluate multi-layered CNNs developed from scratch and popular deep-learning architectures such as Inception v3, ResNET-50, Inception-ResNET-v2, NASNetLarge, ResNeXt-101, ResNeXt-50, and Xception in differentiating between embryos based on their morphological quality at 113 h post insemination (hpi). Xception performed the best in differentiating between the embryos based on their morphological quality., Deep neural networks; Convolutional neural networks; Human embryos; In-vitro fertilization

HIV-1 infection is a major health threat in both developed and developing countries. The integration of mobile health approaches and bioengineered catalytic motors can allow the development of sensitive and portable technologies for HIV-1 management. Here, we report a platform that integrates cellphone-based optical sensing, loop-mediated isothermal DNA amplification and micromotor motion for molecular detection of HIV-1. The presence of HIV-1 RNA in a sample results in the formation of large-sized amplicons that reduce the motion of motors. The change in the motors motion can be accurately measured using a cellphone system as the biomarker for target nucleic acid detection. The presented platform allows the qualitative detection of HIV-1 (n = 54) with 99.1% specificity and 94.6% sensitivity at a clinically relevant threshold value of 1000 virus particles/ml. The cellphone system has the potential to enable the development of rapid and low-cost diagnostics for viruses and other infectious diseases., Micromotors have a range of potential healthcare applications. Here, the authors describe the development of a metal nanoparticle DNA micromotor which can be used to detect human HIV-1 by a change in the motion of the micromotors, monitored by cell phone camera, triggered by binding of HIV-1 RNA.

The diagnosis of keratitis is based on visual exam, tissue cytology, and standard microbial culturing to determine the type of the infectious pathogen. To prescribe appropriate therapy, it is important to distinguish between bacterial, fungal, and viral keratitis, as the treatments are quite different. Diagnosis of the causative organism has a substantial prognostic importance. Further, timely knowledge of the nature of the pathogen is also critical to adapt therapy in patients unresponsive to empiric treatment options, which occurs in 10% of all cases. Currently, the identification of the nature of the pathogen that causes keratitis is achieved via microbial culture screening, which is laboratory-based, expensive, and time-consuming. The most frequent pathogens that cause the corneal ulcers are P. aeruginosa and S. aureus. Here, we report a microchip for rapid (

Rapid and sensitive point-of-care diagnostics are of paramount importance for early detection of infectious diseases and timely initiation of treatment. Here, we present cellulose paper and flexible plastic chips with printed graphene-modified silver electrodes as universal point-of-care diagnostic tools for the rapid and sensitive detection of microbial pathogens or nucleic acids through utilizing electrical sensing modality and loop-mediated isothermal amplification (LAMP). We evaluated the ability of the developed paper-based assay to detect (i) viruses on cellulose-based paper microchips without implementing amplification in samples with viral loads between 106 and 108 copies per ml, and (ii) amplified HIV-1 nucleic acids in samples with viral loads between 10 fg µl−1 and 108 fg µl−1. The target HIV-1 nucleic acid was amplified using the RT-LAMP technique and detected through the electrical sensing of LAMP amplicons for a broad range of RNA concentrations between 10 fg µl−1 and 108 fg µl−1 after 40 min of amplification time. Our assay may be used for antiretroviral therapy monitoring where it meets the sensitivity requirement of the World Health Organization guidelines. Such a paper microchip assay without the amplification step may also be considered as a simple and inexpensive approach for acute HIV detection where maximum viral replication occurs.

The most recent guidelines have called for a significant shift towards viral load testing for HIV/AIDS management in developing countries; however point-of-care (POC) CD4 testing still remains an important component of disease staging in multiple developing countries. The advancements in micro/nanotechnologies and consumer electronics have paved the way for mobile healthcare technologies, and the development of point-of-care smartphone-based diagnostic assays for a variety of disease detection and treatment monitoring. Here, we report a simple, rapid (30 minutes) smartphone-based microfluidic chip for automated CD4 testing using a small volume (30 μL) of whole blood. The smartphone-based device includes an inexpensive (

Certain viruses, such as herpesviruses, are capable of persistent and latent infection of host cells. Distinguishing and separating live, latently infected cells from uninfected cells is not easily attainable using current approaches. The ability to perform such separation would greatly enhance the ability to study primary, infected cells and potentially enable elimination of latently infected cells from the host. Here, the dielectrophoretic response of B cells infected with Kaposi's sarcoma-associated herpesvirus (KSHV) were investigated and compared to uninfected B cells. We evaluated the effect of applied voltage, signal frequency, and flow rate of the sample on the cell capture efficiency. We achieved 37.1% ± 8.5% difference in capture efficiencies between latently KSHV-infected and uninfected BJAB B lymphoma cells at the chip operational conditions of 1V, 50 kHz and 0.02 μl/min sample flow rate. Our results show that latently infected B lymphoma cells demonstrated significantly different electrical response compared to uninfected B cells and DEP-based microchips can be potentially used for sorting latently infected cells based on their electrical properties.

Rapid, sensitive, and selective pathogen detection is of paramount importance in infectious disease diagnosis and treatment monitoring. Currently available diagnostic assays based on polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) are time-consuming, complex, and relatively expensive, thus limiting their utility in resource-limited settings. Loop-mediated isothermal amplification (LAMP) technique has been used extensively in the development of rapid and sensitive diagnostic assays for pathogen detection and nucleic acid analysis and hold great promise for revolutionizing point-of-care molecular diagnostics. Here, we review novel LAMP-based lab-on-a-chip (LOC) diagnostic assays developed for pathogen detection over the past several years. We review various LOC platforms based on their design strategies for pathogen detection and discuss LAMP-based platforms still in development and already in the commercial pipeline. This review is intended as a guide to the use of LAMP techniques in LOC platforms for molecular diagnostics and genomic amplifications.

Objective To evaluate the consistency and objectivity of deep neural networks in embryo scoring and making disposition decisions for biopsy and cryopreservation in comparison to grading by highly trained embryologists. Design Prospective double-blind study using retrospective data. Setting U.S.-based large academic fertility center. Patients Not applicable. Intervention(s) Embryo images (748 recorded at 70 hours postinsemination [hpi]) and 742 at 113 hpi) were used to evaluate embryologists and neural networks in embryo grading. The performance of 10 embryologists and a neural network were also evaluated in disposition decision making using 56 embryos. Main Outcome Measures Coefficients of variation (%CV) and measures of consistencies were compared. Results Embryologists exhibited a high degree of variability (%CV averages: 82.84% for 70 hpi and 44.98% for 113 hpi) in grading embryo. When selecting blastocysts for biopsy or cryopreservation, embryologists had an average consistency of 52.14% and 57.68%, respectively. The neural network outperformed the embryologists in selecting blastocysts for biopsy and cryopreservation with a consistency of 83.92%. Cronbach's α analysis revealed an α coefficient of 0.60 for the embryologists and 1.00 for the network. Conclusions The results of our study show a high degree of interembryologist and intraembryologist variability in scoring embryos, likely due to the subjective nature of traditional morphology grading. This may ultimately lead to less precise disposition decisions and discarding of viable embryos. The application of a deep neural network, as shown in our study, can introduce improved reliability and high consistency during the process of embryo selection and disposition, potentially improving outcomes in an embryology laboratory.

The ability to accurately predict ovulation at-home using low-cost point-of-care diagnostics can be of significant help for couples who prefer natural family planning. Detecting ovulation-specific hormones in urine samples and monitoring basal body temperature are the current commonly home-based methods used for ovulation detection; however, these methods, relatively, are expensive for prolonged use and the results are difficult to comprehend. Here, we report a smartphone-based point-of-care device for automated ovulation testing using artificial intelligence (AI) by detecting fern patterns in a small volume (99% accuracy in effectively predicting ovulation.

The fundamental test for male infertility, semen analysis, is mostly a manually performed subjective and time-consuming process and the use of automated systems has been cost prohibitive. We have previously developed an inexpensive smartphone-based system for at-home male infertility screening through automatic and rapid measurement of sperm concentration and motility. Here, we assessed the feasibility of using a similar smartphone-based system for laboratory use in measuring: a) Hyaluronan Binding Assay (HBA) score, a quantitative score describing the sperm maturity and fertilization potential in a semen sample, b) sperm viability, which assesses sperm membrane integrity, and c) sperm DNA fragmentation that assesses the degree of DNA damage. There was good correlation between the manual analysis and smartphone-based analysis for the HBA score when the device was tested with 31 fresh, unprocessed human semen samples. The smartphone-based approach performed with an accuracy of 87% in sperm classification when the HBA score was set at manufacturer's threshold of 80. Similarly, the sperm viability and DNA fragmentation tests were also shown to be compatible with the smartphone-based system when tested with 102 and 47 human semen samples, respectively.

Zika virus (ZIKV) infection is an emerging pandemic threat to humans that can be fatal in newborns. Advances in digital health systems and nanoparticles can facilitate the development of sensitive and portable detection technologies for timely management of emerging viral infections. Here we report a nanomotor-based bead-motion cellphone (NBC) system for the immunological detection of ZIKV. The presence of virus in a testing sample results in the accumulation of platinum (Pt)-nanomotors on the surface of beads, causing their motion in H(2)O(2) solution. Then the virus concentration is detected in correlation with the change in beads motion. The developed NBC system was capable of detecting ZIKV in samples with virus concentrations as low as 1 particle/μL. The NBC system allowed a highly specific detection of ZIKV in the presence of the closely related dengue virus and other neurotropic viruses, such as herpes simplex virus type 1 and human cytomegalovirus. The NBC platform technology has the potential to be used in the development of point-of-care diagnostics for pathogen detection and disease management in developed and developing countries.