Intelligent Screening Research Articles

Automated pretreatment of environmental water samples and non-targeted intelligent screening of organic compounds based on machine experiments

The complexity of environmental pollutants poses significant challenges for monitoring and analysis, especially with the emergence of numerous emerging contaminants. Traditional analysis methods rely mainly on laboratory analysis, which involves labor-intensive and time-consuming sample preparation procedures and non-target data analysis, greatly limiting the rapid detection of water organic pollutants. In this study, we designed a robot experimenter combined with GC × GC-TOFMS. By configuring self-developed automated analysis software, we established a fully automated process from sample collection to data characterization, for the analysis of organic pollutants. We validated the method with 111 organic standards compounds. The robot performed 2577 actions covering the entire workflow, from water sample collection to sample pre-treatment. The integration of mass spectrometry and related software enabled the automatic analysis of emerging hazardous contaminants, from sampling to the output of detection results. The results showed the automated process could qualitatively identify all compounds and demonstrated good linearity, low detection limits, and excellent quantitative ability within the range of 0.04–0.4 mg/L. The average recoveries of 82.89 % of the samples ranged from 70 % to 120 % (relative standard deviation (RSD) <15 %) at different spiked concentrations. This indicated that the established method could be used for non-targeted analysis of emerging contaminants in environmental water samples. We applied the method to samples from wastewater treatment plants and river sections, identifying 1,902 compounds across 26 categories, including 6 known hazardous contaminants found in all samples. The relative content of these characteristic compounds will inform whether treated wastewater meets discharge standards and aid in tracing the sources of pollutants. Therefore, the development of this fully automated machine experimental method enables real-time and online automatic analysis of organic pollutants in environmental water. The establishment of characteristic fingerprints can provide technical support for early warning and traceability of water quality.

A Study on the Effects of Memory Training and Speech Processing Intervention on Children's Working Memory and Speech Skills: Based on the Perspective of Brain and Cognitive Science

The present study examined the effect of phonological processing and working memory intervention on the verbal working memory and phonological skills of children with Specific reading disorders. Ten participants between 7 to 10 years of age who met ICD 10 criteria for specific reading disorders, were assigned to an experimental group, or a control group. Intelligence screening was done using the Colored Progressive Matrices. Pre - test and post - test measures were the Test of Memory for Children (TOMC) and Informal Reading Assessment (IRA). The intervention group received 30 sessions of phonological processing intervention and working memory intervention. The control group received no intervention. Post - test assessments included TOMC and IRA. Participants who had received the intervention showed more improvement than controls. The majority of the participants in the experimental group performed higher post - intervention.

Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa.

Labeling color fundus photos (CFP) is an important step in the development of artificial intelligence screening algorithms for the detection of diabetic retinopathy (DR). Most studies use the International Classification of Diabetic Retinopathy (ICDR) to assign labels to CFP, plus the presence or absence of macular edema (ME). Images can be grouped as referrable or nonreferrable according to these classifications. There is little guidance in the literature about how to collect and use metadata as a part of the CFP labeling process. This study aimed to improve the quality of the Multimodal Database of Retinal Images in Africa (MoDRIA) by determining whether the availability of metadata during the image labeling process influences the accuracy, sensitivity, and specificity of image labels. MoDRIA was developed as one of the inaugural research projects of the Mbarara University Data Science Research Hub, part of the Data Science for Health Discovery and Innovation in Africa (DS-I Africa) initiative. This is a crossover assessment with 2 groups and 2 phases. Each group had 10 randomly assigned labelers who provided an ICDR score and the presence or absence of ME for each of the 50 CFP in a test image with and without metadata including blood pressure, visual acuity, glucose, and medical history. Specificity and sensitivity of referable retinopathy were based on ICDR scores, and ME was calculated using a 2-sided t test. Comparison of sensitivity and specificity for ICDR scores and ME with and without metadata for each participant was calculated using the Wilcoxon signed rank test. Statistical significance was set at P<.05. The sensitivity for identifying referrable DR with metadata was 92.8% (95% CI 87.6-98.0) compared with 93.3% (95% CI 87.6-98.9) without metadata, and the specificity was 84.9% (95% CI 75.1-94.6) with metadata compared with 88.2% (95% CI 79.5-96.8) without metadata. The sensitivity for identifying the presence of ME was 64.3% (95% CI 57.6-71.0) with metadata, compared with 63.1% (95% CI 53.4-73.0) without metadata, and the specificity was 86.5% (95% CI 81.4-91.5) with metadata compared with 87.7% (95% CI 83.9-91.5) without metadata. The sensitivity and specificity of the ICDR score and the presence or absence of ME were calculated for each labeler with and without metadata. No findings were statistically significant. The sensitivity and specificity scores for the detection of referrable DR were slightly better without metadata, but the difference was not statistically significant. We cannot make definitive conclusions about the impact of metadata on the sensitivity and specificity of image labels in our study. Given the importance of metadata in clinical situations, we believe that metadata may benefit labeling quality. A more rigorous study to determine the sensitivity and specificity of CFP labels with and without metadata is recommended.

159 External Validation of an Artificial Intelligence Screening Tool for Interstitial Lung Disease in Patients Receiving Lung Stereotactic Ablative Radiotherapy

159 External Validation of an Artificial Intelligence Screening Tool for Interstitial Lung Disease in Patients Receiving Lung Stereotactic Ablative Radiotherapy

Detecting key genes relative expression orderings as biomarkers for machine learning-based intelligent screening and analysis of type 2 diabetes mellitus

Type 2 diabetes (T2D), a prevalent chronic metabolic disease of public health concern, is growing rapidly worldwide. In order to characterize robust transcriptomic differences between T2D and the healthy pancreatic islets, we proposed a novel data analysis approach based on the within-sample relative expression orderings of genes (REOs) and machine learning, which can efficiently integrate data from diverse gene expression datasets and help discover some potential signatures. We first identified the overlapped disease-specific reverse REOs from three bulk islets expression profiles that are substantially distinct. Subsequently, the minimum-Redundancy-Maximum-Relevance (mRMR) combined with incremental feature selection (IFS) strategy was applied to select the optimal reverse REOs by using the support vector machine (SVM). As a result, 7 optimal reverse REOs encompassing 12 genes were found, which could produce encouraging predictive performance on training data, independent testing, and external islets data, showcasing robust prediction capability and superior generalization despite the heterogeneity of data. Furthermore, the analysis of differential expression gene (DEG) in bulk islets data and pseudobulk method in single-cell RNA sequencing (scRNA-seq) islets data reaffirmed the crucial roles of these 7 REOs in islets function and disease progression. In conclusion, these REOs are robust biomarkers in T2D and may also be potential therapeutic targets for T2D. We hope these findings can help elucidate the underlying pathogenic mechanisms of T2D and contribute to the treatment of diabetes. The source code is available at https://github.com/xiexq007/T2D-REO.

A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods.

With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification. We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included. These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%. Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

Outpatient prescription drug interactions at a city-affiliated healthcare center in Vietnam

Context: Adverse drug interactions increase the risk of hospitalization, treatment costs, unfavorable treatment outcomes, and prolonged hospital stays. However, there are a limited number of studies conducted in city-affiliated healthcare settings in Vietnam. Aims: To identify drug interactions in outpatient treatment prescriptions at a city-based healthcare center in the southern region of Vietnam. Methods: A cross-sectional descriptive study was conducted on outpatient treatment prescriptions stored in the outpatient department of a city-affiliated healthcare center from January to June, 2023. The study used two databases for drug interaction checking: the Drug Interactions Checker and Medscape Interactions Checker. Univariate and multivariate regression analyses were used to identify predictors of clinically significant drug interactions. Results: Among 1203 surveyed prescriptions, 36.7% contained at least one clinically-significant drug interaction, with 3% of prescriptions having severe interactions. The highest frequency of drug interactions involved amlodipine and metformin (9.9%). Patients with five or more prescribed medications (OR = 3.305; 95% CI: 2.320-4.708), hypertension (OR = 7.142; 95% CI: 4.658-10.950), dyslipidemia (OR = 2.242; 95% CI: 1.665-3.020), and non-insulin-dependent diabetes (OR = 2.447; 95% CI: 1.817-3.295) had a higher likelihood of experiencing clinically significant drug interactions. Conclusions: This study revealed that over one-third of outpatient prescriptions were associated with clinically-significant drug interactions. This study highlights the importance of clinical pharmacists screening prescriptions to detect and reduce interactions, as well as the application of computerized alert systems with intelligent screening software to minimize drug interactions.

Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.

Using Intelligent Screening Service Platform (ISSP) to improve the screening process of clinical trial subjects during COVID-19 pandemic: an experimental study

Using Intelligent Screening Service Platform (ISSP) to improve the screening process of clinical trial subjects during COVID-19 pandemic: an experimental study

Effect of a Reading Intervention on Reading Ability among Children with Specific Reading Disorder

The present study examined the effect of a reading intervention on the reading ability of children with specific reading disorders Ten participants between 7 to 10 years of age who met ICD-10 criteria for specific reading disorder were randomly assigned to an experimental group, or a control group. Intelligence screening was done using the Colored Progressive Matrices. Pre-test and post-test assessment was conducting using the NIMHANS Index for SLD. The intervention group received an 8–9-week reading intervention, 30 minutes per day, on every alternative day of the week. The control group received no intervention. Participants who had received the intervention showed more improvement in reading than controls, and these gains were maintained in a follow up assessment. The majority of the participants in the experimental group performed at their expected grade level on the reading test of NIMHANS SLD post-intervention.

An enumerative pre-processing approach for retinopathy severity grading using an interpretable classifier: a comparative study.

Diabetic retinopathy (DR) is a serious eye complication that results in permanent vision damage. As the number of patients suffering from DR increases, so does the delay in treatment for DR diagnosis. To bridge this gap, an efficient DR screening system that assists clinicians is required. Although many artificial intelligence (AI) screening systems have been deployed in recent years, accuracy remains a metric that can be improved. An enumerative pre-processing approach is implemented in the deep learning model to attain better accuracies for DR severity grading. The proposed approach is compared with various pre-trained models, and the necessary performance metrics were tabulated. This paper also presents the comparative analysis of various optimization algorithms that are utilized in the deep network model, and the results were outlined. The experimental results are carried out on the MESSIDOR dataset to assess the performance. The experimental results show that an enumerative pipeline combination K1-K2-K3-DFNN-LOA shows better results when compared with other combinations. When compared with various optimization algorithms and pre-trained models, the proposed model has better performance with maximum accuracy, precision, recall, F1 score, and macro-averaged metric of 97.60%, 94.60%, 98.40%, 94.60%, and 0.97, respectively. This study focussed on developing and implementing a DR screening system on color fundus photographs. This artificial intelligence-based system offers the possibility to enhance the efficacy and approachability of DR diagnosis.

Dynamic optimal control of coal tar chemical looping gasification based on process modelling and intelligent screening

Aiming to solve the problem of low utilization and poor economic efficiency of coal tar, this study proposes a new method to convert coal tar into syngas that can be used for coal-to-oil production through chemical looping gasification reactions, thus reducing the cost of coal-to-oil production. This proposal converts tar to syngas for Fischer-Tropsch reaction through chemical looping gasification cleaning process, which is realized by process simulation and artificial intelligence prediction to ensure the stable products quality under feed jump abnormal conditions. The proposed process model is first built and the optimal operating parameters are determined by sensitivity analysis after the optimization sequence is determined from correlation analysis. A dynamic model based on the optimal steady-state model is second generated to design an optimal control scheme to efficiently deal with the unusual conditions of the feed step. The data-driven deep learning model LSTM is used to intelligently screen the optimal proportional control scheme with the advantage of low time and cost consumption. Finally, the optimal control scheme is obtained by combining the prediction results of the LSTM model and the controller additions of other important parameters, and the control effect is tested by simulating the tar feed leap conditions proves that the control6 scheme is highly resistant to disturbances. The method achieves 84.1 % high carbon conversion rate of medium-low temperature tar and 88.14 % excellent selectivity of syngas. At the same time, the adjusting of feed step condition with long-term and short-term memory network promotes the development of process design with the low time and cost consumption.

Autonomous Data Association and Intelligent Information Discovery Based on Multimodal Fusion Technology

The effective association of multimodal data is the basis of massive multi-source heterogeneous data sharing in the era of big data. How to realize data autonomous association between massive multimodal databases and the automatic intelligent screening of valuable information from associated data, so as to provide a reliable data source for artificial intelligence (AI), is an urgent problem to be solved. In this paper, a data autonomous association method based on the organizational structure of data cells is proposed, including transaction abstraction based on information nucleuses, symmetric and asymmetric data association based on strategies and data pipes, and information generation based on big data. To screen meaningful data associations, an information-driven intelligent information discovery method and a task-driven intelligent information discovery method are proposed. The former screens meaningful data associations by training the reward and punishment model to simulate the manual scoring of data associations. The latter is task-oriented and screens meaningful data associations by training the reward and punishment model to simulate the manual ranking of data associations related to the task requests. Through the above work, autonomous data association and intelligent information discovery are effectively realized based on multimodal fusion technology, which provides a novel data source mining approach using multimodal data sharing and intelligent information discovery.

Harmful communication behaviors in cancer care: Asystematic review of patients and family caregivers perspectives.

Issues regarding clinician communication remain an important source of complaints within healthcare. This systematic review aims to determine cancer patients' and their family caregivers' views on which clinicians' communication behaviors can harm (i.e. eliciting negative feelings/consequences for patients/family caregivers). We searched for all types of peer-reviewed studies that determined adult (≥18years) cancer patients' and/or family caregivers' perspectives on which clinicians' communication behaviors can harm in several databases (PubMed, Embase, Web of Science, Cochrane Library, Emcare, PsycINFO and Academic Search Premier), supplemented by expert-consultation. Studies were screened using the Artificial intelligence screening tool of ASReview and data was analyzed using Thematic Analysis. To assess the quality of the studies the Qualsyst critical appraisal tool was used. A total of 47 studies were included. Four main themes of harmful communication behaviors were identified: (1) Lack of tailored information provision (e.g. giving too little or too much/specific information) (2) Lack of tailored decision making (ranging from; patient exclusion, to the patients' responsibility, and/or haste) (3) Lack of feeling seen and heard (seen as a disease, not as a human being; not listened to concerns and emotions) (4) Lack of feeling held and remembered (forgotten agreements; lack of care continuity). Our results reveal an overview of patients' and family caregivers' perspectives on which clinicians' communication behaviors can harm. Harm could be prevented when information and decision involvement are tailored and patients' and family caregivers' needs to feel seen, heard, held and remembered are met.

A quick and intelligent screening method for large-scale retired batteries based on cloud-edge collaborative architecture

Rapid and accurate sorting consistent cells is indispensable for the cascade utilization of retired batteries. However, the conventional full charge-discharge capacity testing methods are time-consuming and low efficient, which cannot be employed to screen large-scale retired batteries. Moreover, most existing artificial intelligence techniques sort retired batteries at cloud servers, which leads to tremendous burdens of transmission. Therefore, a fast and intelligent screening approach based on a Light Gradient Boosting Machine is proposed, which only utilizes partial charge/discharge curves, significantly reducing the test time. Meanwhile, a cloud-edge collaborative framework for separating large-scale retired batteries is developed. Specifically, the model is trained in the cloud and screens retired batteries in the edge plane. The experimental results show that the data transmission time of the cloud-edge collaborative framework is reduced by about 3 orders of magnitude than the cloud computing plane. Besides, the method achieves a 97 % classification accuracy rate. More importantly, the screening efficiency of the proposed approach is approximately 6 times higher than that of the conventional full charge-discharge testing methods. Additionally, the voltage consistency of the new module regrouped is significantly improved compared with the original module. The mean of the standard deviation is reduced by approximately 3 times.

Measurement scales of mental health related to climate change: a scoping review protocol using artificial intelligence

IntroductionHuman actions have influenced climate changes around the globe, causing extreme weather phenomena and impacting communities worldwide. Climate change has caused, directly or indirectly, health effects such as injury and...

Development of a new CO2 EOR screening approach focused on deep-depth reservoirs

Carbon dioxide (CO2) enhanced oil recovery (EOR) is an important technology with the dual benefits of increasing oil production and reducing greenhouse gas emissions. Reservoir screening is crucial for the successful application of CO2 EOR; however, determining CO2 EOR feasibility for a specific field can be a time- and resource-intensive process. Traditional screening criteria can be overly arbitrary, making it difficult to accurately assess whether a field is suitable for CO2 EOR—failing to meet a single requirement may result in an unsuitable judgment. Existing intelligent screening tools primarily focus on classification algorithms to develop predictive models for CO2 EOR feasibility. While these tools are accurate for shallow-depth reservoirs, they struggle to predict CO2 EOR in mid- and deep-depth reservoirs accurately. In this study, an EOR database was created based on 464 EOR projects from 18 countries. Reservoir data from the Williston Basin, TORIS (Tertiary Oil Recovery Information System), and Alberta Basin were also collected to further validate the feasibility of the proposed approach. A new screening criterion was developed based on the boxplot analysis results of the collected worldwide EOR projects and existing CO2 EOR screening guidelines. Weight factors for parameters were determined using the importance permutation technique and the proposed classification algorithms to minimize bias. An innovative CO2 EOR scoring approach was developed using membership functions, composite screening scores, and the six machine learning algorithms. The results showed high prediction accuracy for the worldwide EOR projects database with R-squared values ranging from 0.91 to 0.98. The proposed screening system was further employed to evaluate the prediction accuracy for the mid- and deep-depth reservoirs. The results showed prediction rates ranging from 86% to 92% compared to the analytical solutions. Among these six regression models, random forest outperformed the others with the most stable performance in both the testing phase and case studies with the R-squared value and root mean square error of 0.97 and 4.8, respectively. The proposed screening tool can be further applied to provide recommendations on the feasibility of CO2 EOR in mid- and deep-depth reservoirs.

Advances, challenges and outlooks in frost-free air-source heat pumps: A comprehensive review from materials, components to systems

Air-source heat pumps (ASHPs) have dominated the heat pump market, accounting for over 90% of the market share, due to their potential to reduce emissions, low operational costs, and ease of maintenance. However, ASHPs are often inefficient in the summer and prone to frosting in the winter. Frost-free air-source heat pumps (FFASHPs) have recently emerged as a promising solution for both heating and cooling, achieving high efficiency and eliminating frost. This review provides a comprehensive summary of the latest advancements in FFASHPs, focusing on materials, components, and systems. Specifically, we begin by discussing the frost-free mechanism of FFASHPs, followed by a systematic presentation of the solutions used in FFASHPs in terms of their materials and properties. Additionally, we summarize breakthroughs in heat and mass exchangers, including the heat and mass transfer characteristics, models, and coefficients. We also discuss regeneration methods of solutions in terms of driving force, regeneration rate, and efficiency. Furthermore, we demonstrate the principle and potential of energy savings, regional suitability, and design and operational optimization of the system. Finally, we provide insights and perspectives on FFASHPs to further improve their efficiency and promote their application, including the development of a thermophysical property and corrosion database for solutions, intelligent screening of solutions, uniform correlation expressions of heat and mass transfer coefficients, comprehensive analysis and comparison of different regeneration approaches, obtaining dynamic properties of FFASHPs for predictive control and dynamic programming, and developing design standards for FFASHPs.

110-OR: Autonomous Artificial Intelligence Diabetic Eye Exams to Mitigate Disparities in Screening Completion

Diabetic retinopathy (DR) is a complication of diabetes that can result in vision loss, but early detection and treatment through screening can prevent this. Few individuals with diabetes meet recommended DR screening guidelines, and racial/ethnic minority youth are less likely to undergo recommended screening. We sought to determine if implementing point of care (POC) autonomous artificial intelligence (AI) screening could mitigate disparities in diabetic eye exam completion. In a preregistered prospective study, ACCESS2, youth with type 1 and type 2 diabetes meeting American Diabetes Association criteria for needing DR screening underwent point of care autonomous AI diabetic eye exams at diabetes clinic visits. Completion rates of diabetic eye screening exams were compared prior to and after implementation of autonomous AI using chi-square tests. A total of 152 youth with T1D (69.7%) and T2D (30.3%) were enrolled, mean age 15.5y, 45.4% non-Hispanic (NH) White, with duration of diabetes of 5.7y, and median HbA1c of 8%. A greater percentage of NH White participants reported any prior diabetic eye exam compared to non-white and Hispanic participants (92.8% v 65.1%, p&amp;lt;0.001). Multivariable analysis demonstrated that even when controlling for age, sex, HbA1c, Medicaid insurance, diabetes type and duration of diabetes, non-white youth are less likely to have had a prior diabetic eye exam (OR 0.26, CI: 0.07-0.93, p=0.04). After undergoing POC autonomous AI diabetic eye exams, completion rates were 99% for participants in all subgroups. Implementation of autonomous AI at the point of care increases access to and completion of diabetic eye exams, and promotes health equity for minority youth with diabetes. Disclosure A.Zehra: None. M.D.Abràmoff: Board Member; Digital Diagnostics, Consultant; AbbVie Inc., NovaGo Therapeutics AG, Other Relationship; Digital Diagnostics, Stock/Shareholder; Digital Diagnostics. R.M.Wolf: Research Support; Dexcom, Inc., Boehringer Ingelheim Inc. L.A.Bromberger: None. B.Pan: None. A.Shehadeh: None. D.Patel: None. E.A.Brown: None. R.Channa: None. T.Liu: None. H.Lehmann: None. Funding National Eye Institute (R01EY033233, 5K23EY030911-03)