Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI)

PURPOSE

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS

AI-based QA tools could provide accurate, reproducible, and efficient staff performance monitoring in an ART practice.

Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks

PURPOSE

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).

METHODS

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.

RESULTS

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.

CONCLUSION

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.

The use of voting ensembles to improve the accuracy of deep neural networks as a non-invasive method to predict embryo ploidy status

PURPOSE

To determine if creating voting ensembles combining convolutional neural networks (CNN), support vector machine (SVM), and multi-layer neural networks (NN) alongside clinical parameters improves the accuracy of artificial intelligence (AI) as a non-invasive method for predicting aneuploidy.

METHODS

A cohort of 699 day 5 PGT-A tested blastocysts was used to train, validate, and test a CNN to classify embryos as euploid/aneuploid. All embryos were analyzed using a modified FAST-SeqS next-generation sequencing method. Patient characteristics such as maternal age, AMH level, paternal sperm quality, and total number of normally fertilized (2PN) embryos were processed using SVM and NN. To improve model performance, we created voting ensembles using CNN, SVM, and NN to combine our imaging data with clinical parameter variations. Statistical significance was evaluated with a one-sample t-test with 2 degrees of freedom.

RESULTS

When assessing blastocyst images alone, the CNN test accuracy was 61.2% (± 1.32% SEM, n = 3 models) in correctly classifying euploid/aneuploid embryos (n = 140 embryos). When the best CNN model was assessed as a voting ensemble, the test accuracy improved to 65.0% (AMH; p = 0.1), 66.4% (maternal age; p = 0.06), 65.7% (maternal age, AMH; p = 0.08), 66.4% (maternal age, AMH, number of 2PNs; p = 0.06), and 71.4% (maternal age, AMH, number of 2PNs, sperm quality; p = 0.02) (n = 140 embryos).

CONCLUSIONS

By combining CNNs with patient characteristics, voting ensembles can be created to improve the accuracy of classifying embryos as euploid/aneuploid from CNN alone, allowing for AI to serve as a potential non-invasive method to aid in karyotype screening and selection of embryos.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip

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-1 in phosphate buffered saline (PBS), 0.43 ng mL-1 in human serum and 0.64 ng mL-1 in artificial human urine). Image-based inferences were made by our adversarial-based SPyDERMAN network that was developed using a limited dataset of 104 smartphone images of microchips with bubble signals from tests performed with known fentanyl concentrations and using our retrospective library of 17 573 non-specific bubbling-microchip images. The accuracy (± standard error of mean) of the developed system in determining the presence of fentanyl, when using a cutoff concentration of 1 ng mL-1, was 93 ± 0% in human serum (n = 100) and 95.3 ± 1.5% in artificial human urine (n = 100).

Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study

PURPOSE

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

Validation of a smartphone-based device to measure concentration, motility and morphology in swine ejaculates

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.

P-294 Use of Artificial Intelligence to Assess the Effects of Assisted Hatching on Embryo Development and Implantation Potential

Study question

Does the use of laser-assisted hatching (AH) on cleavage stage embryos affect in vitro preimplantation embryo development or implantation potential?

Summary answer

There is no difference in blastocyst conversion rate or implantation potential of embryos following AH at the cleavage stage for patients under age 35 years.

What is known already

Laser-AH is the process of creating an opening within the zona pellucida on cleavage stage embryos to facilitate biopsy of trophectoderm cells for preimplantation genetic testing (PGT). Studies have shown that PGT for aneuploidy (PGT-A) in patients under 35 years have reduced pregnancy rates compared to those not undergoing biopsy. This is attributed to the additional micromanipulation events involved with PGT-A may decrease the viability of embryos and compromise their implantation potential. We aimed to objectively compare the impact of AH on embryo development using an artificial intelligence (AI)-algorithm trained to assess embryo quality and predict developmental fate.

Study design, size, duration

A retrospective dataset from patients under 35 years was generated from two timepoints: cleavage stage embryos immediately before AH between 60-64 hours post insemination (hpi); and blastocyst stage embryos between 110-115 hpi prior to transfer or vitrification. Time-lapse imaging was obtained using the EmbryoScope (Vitrolife). Cleavage stage embryo images were used to train a convolutional neural networks (CNN) to predict and classify the development and implantation potential of cleavage and blastocyst stage embryos.

Participants/materials, setting, methods

Time-lapse images were collected for 1444 cleavage stage embryos spanning 189 in vitro fertilization (IVF) cycles between January 2014 – December 2021 at a single academic fertility center in Boston. Embryos were categorized into two groups: Day 3 embryos with AH (D3+AH) and without AH (D3-No AH). Each patient had a single blastocyst embryo transfer with a known outcome. Two-tailed t-tests were used to compare differences, with p-value less than 0.05 set for statistical significance.

Main results and the role of chance

The dataset included 1035 embryos with AH (D3+AH) and 409 embryos without AH (D3-No AH). There were no differences in AI-predicted blastocyst development between Day 3 embryos with AH and without AH (64.1% vs 64.1%) or AI-predicted high quality blastocyst development rate between these two groups (43.8% vs 40.8%), respectively. On Day 5 there were no differences in the AI-categorization of embryos at the blastocyst stage between embryos with or without AH (62.3% vs 62.5%) or AI-categorization of high-quality blastocyst development (45.2% vs 41.8%), respectively. AI predicted a similar implantation potential between embryos with and without AH at the cleavage stage (61.1% vs 69.9%). When stratifying to only the embryos transferred, there were no differences in the AI-predicted blastocyst development between Day 3 embryos with AH and without AH (96.0% vs 97.1%) or in the AI-predicted high quality blastocyst development rate between these two groups (72.0% vs 82.7%). AI predicted a similar implantation potential between embryos with and without AH at the cleavage stage (72.0% vs 69.0%). These results correspond with the true clinical pregnancy rate between the AH and Non-AH groups (68.0% vs 61.9%, p = 0.44).

Limitations, reasons for caution

These retrospective findings were of patients who had time-lapse imaging of cleavage stage and blastocysts available. Additionally, we focused on high prognosis patients that were eligible for single blastocyst stage embryo transfer. Clinical pregnancy rate was examined, not spontaneous abortion or live birth rates.

Wider implications of the findings

Utilization of AI technology allows for more objective and standardized methods for examining the impact of laboratory procedures on the developmental fate of embryos. This study demonstrated the safety of utilizing laser-assisted hatching on embryo development within this study population.

Trial registration number

None

SARS-CoV-2 RNA Detection by a Cellphone-Based Amplification-Free System with CRISPR/CAS-Dependent Enzymatic (CASCADE) Assay

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.

O-125 Development of an artificial intelligence embryo witnessing system to accurately track and identify patient specific embryos in a human IVF laboratory

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.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Artificial intelligence (AI) systems have been proposed for reproductive medicine since 1997. Although AI is the main driver of emergent technologies in reproduction, such as robotics, Big Data, and internet of things, it will continue to be the engine for technological innovation for the foreseeable future. What does the future of AI research look like?

Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory

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.

Customizing the Protoscolicidal Activity by a Drug Delivery System: Application of Guar Gum in Electrospun Nanofibers

Background

The present study aimed to control mebendazole drug release from ethyl cellulose nanofibers containing guar gum produced by Electrospinning Method (ESM) on mortality of hydatid cyst protoscoleces under laboratory conditions.

Methods

The study was conducted in Arak Islamic Azad University, 2019. After preparation of ethyl cellulose nanofibers containing guar gum with concentrations 10, 250, 50 and 500 ppm with ESM, the uniformity and fineness of nanofibers were investigated by electron microscope. By determining the absorption of nanofibers during 312 h via spectrophotometry method, the amount of drug release was obtained. Then, the mortality of live protoscoleces in-vitro with nanofibers made with different concentrations was studied during 13 days.

Results

Guar gum nanofiber with four concentrations of 10, 50, 250 and 500 ppm had 0.78512, 0.83729, 1.0098 and 1.0633 absorption respectively and showed drug release 42.09%, 39.95%, 33.05% and 30.96% after 312 hours. Therefore, the survival of protoscoleces in the presence of guar gum with four concentrations was zero after 3, 6, 11 and 13 days (P<0.05).

Conclusion

To produce nanofibers carrying the drug for research related to the treatment of hydatid cysts, the electrospinning technique can be considered as a reliable method.

Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological 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.

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning

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.

Virus detection using nanoparticles and deep neural network-enabled smartphone system

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%.

Performance of a deep learning based neural network in the selection of human blastocysts for implantation

Deep learning in in vitro fertilization is currently being evaluated in the development of assistive tools for the determination of transfer order and implantation potential using time-lapse data collected through expensive imaging hardware. Assistive tools and algorithms that can work with static images, however, can help in improving the access to care by enabling their use with images acquired from traditional microscopes that are available to virtually all fertility centers. Here, we evaluated the use of a deep convolutional neural network (CNN), trained using single timepoint images of embryos collected at 113 hr post-insemination, in embryo selection amongst 97 clinical patient cohorts (742 embryos) and observed an accuracy of 90% in choosing the highest quality embryo available. Furthermore, a CNN trained to assess an embryo's implantation potential directly using a set of 97 euploid embryos capable of implantation outperformed 15 trained embryologists (75.26% vs. 67.35%, p<0.0001) from five different fertility centers.

Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?

Goal: The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed non-infected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

Consistency and objectivity of automated embryo assessments using deep neural networks

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.

Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology

Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy 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 (<$100 and <$5) and automated imaging platforms that utilize advances in artificial intelligence (AI) for rapid, reliable, and accurate evaluations of embryo morphological qualities. Using a layered learning approach, we have shown that network models pre-trained with high quality embryo image data can be re-trained using data recorded on such low-cost, portable optical systems for embryo assessment and classification when relatively low-resolution image data are used. Using two test sets of 272 and 319 embryo images recorded on the reported stand-alone and smartphone optical systems, we were able to classify embryos based on their cell morphology with >90% accuracy.

Automated smartphone-based system for measuring sperm viability, DNA fragmentation, and hyaluronic binding assay score

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.

An inexpensive smartphone-based device for point-of-care ovulation testing

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 (<100 μL) of saliva that is air-dried on a microfluidic device. We evaluated the performance of the device using artificial saliva and human saliva samples and observed that the device showed >99% accuracy in effectively predicting ovulation.

DNA engineered micromotors powered by metal nanoparticles for motion based cellphone diagnostics

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.

Nanoparticle-enhanced electrical detection of Zika virus on paper microchips

Zika virus (ZIKV) is a reemerging flavivirus causing an ongoing pandemic and public health emergency worldwide. There are currently no effective vaccines or specific therapy for Zika infection. Rapid, low-cost diagnostics for mass screening and early detection are of paramount importance in timely management of the infection at the point-of-care (POC). The current Zika diagnostics are laboratory-based and cannot be implemented at the POC particularly in resource-limited settings. Here, we develop a nanoparticle-enhanced viral lysate electrical sensing assay for Zika virus detection on paper microchips with printed electrodes. The virus is isolated from biological samples using antibodies and labeled with platinum nanoparticles (PtNPs) to enhance the electrical signal. The captured ZIKV-PtNP complexes are lysed using a detergent to release the electrically charged molecules associated with the intact virus and the PtNPs on the captured viruses. The released charged molecules and PtNPs change the electrical conductivity of the solution, which can be measured on a cellulose paper microchip with screen-printed microelectrodes. The results confirmed a highly specific detection of ZIKV in the presence of other non-targeted viruses, including closely related flaviviruses such as dengue virus-1 and dengue virus-2 with a detection limit down to 101 virus particles per μl. The developed assay is simple, rapid, and cost-effective and has the potential for POC diagnosis of viral infections and treatment monitoring.

Hybrid Paper-Plastic Microchip for Flexible and High-Performance Point-of-Care Diagnostics

A low-cost and easy-to-fabricate microchip remains a key challenge for the development of true point-of-care (POC) diagnostics. Cellulose paper and plastic are thin, light, flexible, and abundant raw materials, which make them excellent substrates for mass production of POC devices. Herein, a hybrid paper-plastic microchip (PPMC) is developed, which can be used for both single and multiplexed detection of different targets, providing flexibility in the design and fabrication of the microchip. The developed PPMC with printed electronics is evaluated for sensitive and reliable detection of a broad range of targets, such as liver and colon cancer protein biomarkers, intact Zika virus, and human papillomavirus nucleic acid amplicons. The presented approach allows a highly specific detection of the tested targets with detection limits as low as 10² ng mL^-1 for protein biomarkers, 10³ particle per milliliter for virus particles, and 10² copies per microliter for a target nucleic acid. This approach can potentially be considered for the development of inexpensive and stable POC microchip diagnostics and is suitable for the detection of a wide range of microbial infections and cancer biomarkers.

Motion-Based Immunological Detection of Zika Virus Using Pt-Nanomotors and a Cellphone

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 H₂O₂ 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.

Applications of gold nanoparticles in virus detection

Viruses are the smallest known microbes, yet they cause the most significant losses in human health. Most of the time, the best-known cure for viruses is the innate immunological defense system of the host; otherwise, the initial prevention of viral infection is the only alternative. Therefore, diagnosis is the primary strategy toward the overarching goal of virus control and elimination. The introduction of a new class of nanoscale materials with multiple unique properties and functions has sparked a series of breakthrough applications. Gold nanoparticles (AuNPs) are widely reported to guide an impressive resurgence in biomedical and diagnostic applications. Here, we review the applications of AuNPs in virus testing and detection. The developed AuNP-based detection techniques are reported for various groups of clinically relevant viruses with a special focus on the applied types of bio-AuNP hybrid structures, virus detection targets, and assay modalities and formats. We pay particular attention to highlighting the functional role and activity of each core Au nanostructure and the resultant detection improvements in terms of sensitivity, detection range, and time. In addition, we provide a general summary of the contributions of AuNPs to the mainstream methods of virus detection, technical measures, and recommendations required in guidance toward commercial in-field applications.

An automated smartphone-based diagnostic assay for point-of-care semen analysis

Male infertility affects up to 12% of the world's male population and is linked to various environmental and medical conditions. Manual microscope-based testing and computer-assisted semen analysis (CASA) are the current standard methods to diagnose male infertility; however, these methods are labor-intensive, expensive, and laboratory-based. Cultural and socially dominated stigma against male infertility testing hinders a large number of men from getting tested for infertility, especially in resource-limited African countries. We describe the development and clinical testing of an automated smartphone-based semen analyzer designed for quantitative measurement of sperm concentration and motility for point-of-care male infertility screening. Using a total of 350 clinical semen specimens at a fertility clinic, we have shown that our assay can analyze an unwashed, unprocessed liquefied semen sample with <5-s mean processing time and provide the user a semen quality evaluation based on the World Health Organization (WHO) guidelines with ~98% accuracy. The work suggests that the integration of microfluidics, optical sensing accessories, and advances in consumer electronics, particularly smartphone capabilities, can make remote semen quality testing accessible to people in both developed and developing countries who have access to smartphones.

Strategies in Ebola virus disease (EVD) diagnostics at the point of care

Ebola virus disease (EVD) is a devastating, highly infectious illness with a high mortality rate. The disease is endemic to regions of Central and West Africa, where there is limited laboratory infrastructure and trained staff. The recent 2014 West African EVD outbreak has been unprecedented in case numbers and fatalities, and has proven that such regional outbreaks can become a potential threat to global public health, as it became the source for the subsequent transmission events in Spain and the USA. The urgent need for rapid and affordable means of detecting Ebola is crucial to control the spread of EVD and prevent devastating fatalities. Current diagnostic techniques include molecular diagnostics and other serological and antigen detection assays; which can be time-consuming, laboratory-based, often require trained personnel and specialized equipment. In this review, we discuss the various Ebola detection techniques currently in use, and highlight the potential future directions pertinent to the development and adoption of novel point-of-care diagnostic tools. Finally, a case is made for the need to develop novel microfluidic technologies and versatile rapid detection platforms for early detection of EVD.