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In heterozygous mice, attenuation of G-protein-coupled receptor kinase 2 (GRK2) level in nociceptors is associated with enhanced and prolonged inflammatory hyperalgesia. To further elucidate the role of GRK2 in nociceptor function we reversibly decreased GRK2 expression using intrathecal antisense oligodeoxynucleotide (AS-ODN). GRK2 AS-ODN administration led to an enhanced and prolonged hyperalgesia induced by prostaglandin E(2), epinephrine and carrageenan. Moreover, this effect persisted unattenuated 2weeks after the last dose of antisense, well after GRK2 protein recovered, suggesting that transient attenuation of GRK2 produced neuroplastic changes in nociceptor function. Unlike hyperalgesic priming induced by transient activation of protein kinase C epsilon (PKCε), (Aley et al., 2000; Parada et al., 2003b), the enhanced and prolonged hyperalgesia following attenuation of GRK2 is PKCε- and cytoplasmic polyadenylation element binding protein (CPEB)-independent and is protein kinase A (PKA)- and Src tyrosine kinase (Src)-dependent. Finally, rats treated with GRK2 AS-ODN exhibited enhanced and prolonged hyperalgesia induced by direct activation of second messengers, adenyl cyclase, Epac or PKA, suggesting changes downstream of G-protein-coupled receptors. Because inflammation can produce a decrease in GRK2, such a mechanism could help explain a predilection to develop chronic pain, after resolution of acute inflammation.

Genetic and environmental influences on variation in balance performance were measured in 93 monozygous and 83 dizygous female twin pairs aged 21-82 years (mean age, 50.5 years) in Melbourne, Australia, between 1999 and 2003. The authors administered clinical (Lord's Balance Test and Step Test) and laboratory tests of static and dynamic balance from the Chattecx Balance System with and without distractor tasks. The authors conducted factor analysis and estimated genetic and environmental variance components and heritability (defined as additive genetic variance as a proportion of all variance, after adjustment for age) using a multivariate normal model with the statistical package FISHER. Three factors were identified and adjusted for age. Heritability was 46% (standard error (SE), 9) for the 'sensory balance tests' factor and 30% (SE, 9) for the 'static and dynamic perturbations' factor. For both factors, the remaining variance was attributed to unique environmental effects. There was no evidence that genetic factors influenced variation in the 'dynamic weight shift tests' factor, with environmental effects shared by twins accounting for 38% (SE, 7) of variance. Neither genetic nor environmental proportions of variance differed significantly between twin subgroups by age (50 years). An age-related decline in performance measures was found across the whole sample. These results imply that balance impairments may have a heritable element. [ABSTRACT FROM AUTHOR]

Advances in ability to comprehensively record individuals' digital lives and in AI modeling of those data facilitate new possibilities for describing, predicting, and generating a wide variety of behavioral processes. In this paper, we consider these advances from a person-specific perspective, including whether the pervasive concerns about generalizability of results might be productively reframed with respect to transferability of models, and how self-supervision and new deep neural network architectures that facilitate transfer learning can be applied in a person-specific way to the super-intensive longitudinal data arriving in the Human Screenome Project. In developing the possibilities, we suggest Molenaar add a statement to the person-specific Manifesto - "In short, the concerns about generalizability commonly leveled at the person-specific paradigm are unfounded and can be fully and completely replaced with discussion and demonstrations of transferability ."

Health care systems worldwide have been battling the ongoing COVID-19 pandemic. Since the beginning of the COVID-19 pandemic, Lymphocytes and CRP have been reported as markers of interest. We chose to investigate the prognostic value of the LCR ratio as a marker of severity and mortality in COVID-19 infection. Between 1 March and 30 April 2020, we conducted a multicenter, retrospective cohort study of patients with moderate and severe coronavirus disease 19 (COVID-19), all of whom were hospitalized after being admitted to the Emergency Department (ED). We conducted our study in six major hospitals of northeast France, one of the outbreak's epicenters in Europe. A total of 1035 patients with COVID-19 were included in our study. Around three-quarters of them (76.2%) presented a moderate form of the disease, while the remaining quarter (23.8%) presented a severe form requiring admission to the ICU. At ED admission, the median LCR was significantly lower in the group presenting severe disease compared to that with moderate disease (versus 6.24 (3.24-12) versus 12.63 ((6.05-31.67)), p < 0.001). However, LCR was neither associated with disease severity (OR: 0.99, CI 95% (0.99-1)), p = 0.476) nor mortality (OR: 0.99, CI 95% (0.99-1)). In the ED, LCR, although modest, with a threshold of 12.63, was a predictive marker for severe forms of COVID-19.

Increased activation of the contact system protein high molecular weight kininogen (HK) has been shown in plasma and cerebrospinal fluid of Alzheimer's disease (AD) patients, but its potential role in the brain has not been explored. We assessed HK levels in brain tissue from 20 AD patients and controls and modeled the effects of HK on microglia-like cells in culture. We show increased levels of HK in the hippocampus of AD patients, which colocalized with amyloid beta (Aβ) deposits and activated microglia. Treatment of microglia with HK led to cell clustering and elevated levels of phagocytosed Aβ. We demonstrate that microglia internalize HK and traffic it to lysosomes, which is accompanied by reduced activity of lysosomal cathepsins L and S. Our results suggest that HK accumulation in the AD hippocampus may alter microglial uptake and degradation of Aβ fibrils, possibly contributing to microglial dysfunction in AD., (© 2021 the Alzheimer's Association.)

Introduction: Augmented reality (AR) has promise as a clinical teaching tool, particularly for remote learning. The Chariot Augmented Reality Medical (CHARM) simulator integrates real-time communication into a portable medical simulator with a holographic patient and monitor. The primary aim was to analyze feedback from medical and physician assistant students regarding acceptability and feasibility of the simulator., Methods: Using the CHARM simulator, we created an advanced cardiovascular life support (ACLS) simulation scenario. After IRB approval, preclinical medical and physician assistant students volunteered to participate from August to September 2020. We delivered augmented reality headsets (Magic Leap One) to students before the study. Prior to the simulation, via video conference, we introduced students to effective communication skills during a cardiac arrest. Participants then, individually and remotely from their homes, synchronously completed an instructor-led ACLS AR simulation in groups of three. After the simulation, students participated in a structured focus group using a qualitative interview guide. Our study team coded their responses and interpreted them using team-based thematic analysis., Results: Eighteen medical and physician assistant students participated. We identified four domains that reflected trainee experiences: experiential satisfaction, learning engagement, technology learning curve, and opportunities for improvement. Students reported that the simulator was acceptable and enjoyable for teaching trainees communication skills; however, there were some technical difficulties associated with initial use., Conclusion: This study suggests that multiplayer AR is a promising and feasible approach for remote medical education of communication skills during medical crises., Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-022-01598-7., Competing Interests: Competing InterestsTJ Caruso, E Wang, and S Rodriguez are on the board of directors for Invincikids, a federal, tax exempt, non-profit organization that seeks to train providers how to use technologies in healthcare and distribute technologies to sick children. They receive no realized or potential financial benefit from their board positions or the results of this study. On behalf of all authors, the corresponding author states that there is no conflict of interest., (© The Author(s) under exclusive licence to International Association of Medical Science Educators 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces., Objective: We designed a strategy to gamify the collection and labeling of child emotion-enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches., Methods: We leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort, called HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion-centric database (>30 times larger than existing public pediatric emotion data sets), we trained a convolutional neural network (CNN) computer vision classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions evoked by children., Results: The classifier achieved a 66.9% balanced accuracy and 67.4% F1-score on the entirety of the Child Affective Facial Expression (CAFE) as well as a 79.1% balanced accuracy and 78% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously developed classifiers evaluated against CAFE, the best of which reached a 56% balanced accuracy even when combining "anger" and "disgust" into a single class., Conclusions: This work validates that mobile games designed for pediatric therapies can generate high volumes of domain-relevant data sets to train state-of-the-art classifiers to perform tasks helpful to precision health efforts., (©Peter Washington, Haik Kalantarian, John Kent, Arman Husic, Aaron Kline, Emilie Leblanc, Cathy Hou, Onur Cezmi Mutlu, Kaitlyn Dunlap, Yordan Penev, Maya Varma, Nate Tyler Stockham, Brianna Chrisman, Kelley Paskov, Min Woo Sun, Jae-Yoon Jung, Catalin Voss, Nick Haber, Dennis Paul Wall. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 08.04.2022.)

Bulk RNA sequencing provides the opportunity to understand biology at the whole transcriptome level without the prohibitive cost of single cell profiling. Advances in spatial transcriptomics enable to dissect tissue organization and function by genome-wide gene expressions. However, the readout of both technologies is the overall gene expression across potentially many cell types without directly providing the information of cell type constitution. Although several in-silico approaches have been proposed to deconvolute RNA-Seq data composed of multiple cell types, many suffer a deterioration of performance in complex tissues. Here we present AdRoit, an accurate and robust method to infer the cell composition from transcriptome data of mixed cell types. AdRoit uses gene expression profiles obtained from single cell RNA sequencing as a reference. It employs an adaptive learning approach to alleviate the sequencing technique difference between the single cell and the bulk (or spatial) transcriptome data, enhancing cross-platform readout comparability. Our systematic benchmarking and applications, which include deconvoluting complex mixtures that encompass 30 cell types, demonstrate its preferable sensitivity and specificity compared to many existing methods as well as its utilities. In addition, AdRoit is computationally efficient and runs orders of magnitude faster than most methods., (© 2021. The Author(s).)

Background/introduction: Emotion detection classifiers traditionally predict discrete emotions. However, emotion expressions are often subjective, thus requiring a method to handle compound and ambiguous labels. We explore the feasibility of using crowdsourcing to acquire reliable soft-target labels and evaluate an emotion detection classifier trained with these labels. We hypothesize that training with labels that are representative of the diversity of human interpretation of an image will result in predictions that are similarly representative on a disjoint test set. We also hypothesize that crowdsourcing can generate distributions which mirror those generated in a lab setting., Methods: We center our study on the Child Affective Facial Expression (CAFE) dataset, a gold standard collection of images depicting pediatric facial expressions along with 100 human labels per image. To test the feasibility of crowdsourcing to generate these labels, we used Microworkers to acquire labels for 207 CAFE images. We evaluate both unfiltered workers as well as workers selected through a short crowd filtration process. We then train two versions of a ResNet-152 neural network on soft-target CAFE labels using the original 100 annotations provided with the dataset: (1) a classifier trained with traditional one-hot encoded labels, and (2) a classifier trained with vector labels representing the distribution of CAFE annotator responses. We compare the resulting softmax output distributions of the two classifiers with a 2-sample independent t-test of L1 distances between the classifier's output probability distribution and the distribution of human labels., Results: While agreement with CAFE is weak for unfiltered crowd workers, the filtered crowd agree with the CAFE labels 100% of the time for happy, neutral, sad and "fear + surprise", and 88.8% for "anger + disgust". While the F1-score for a one-hot encoded classifier is much higher (94.33% vs. 78.68%) with respect to the ground truth CAFE labels, the output probability vector of the crowd-trained classifier more closely resembles the distribution of human labels (t=3.2827, p=0.0014)., Conclusions: For many applications of affective computing, reporting an emotion probability distribution that accounts for the subjectivity of human interpretation can be more useful than an absolute label. Crowdsourcing, including a sufficient filtering mechanism for selecting reliable crowd workers, is a feasible solution for acquiring soft-target labels.

The evaluation by the emergency doctor of the patient presenting with a psychiatric symptom before being taken care of by the psychiatric team is described by the term medical clearance. There is little work on the performance of complementary examinations on these patients. A retrospective multicentre study conducted at the Metz-Thionville regional hospital (57) shows that at least one complementary examination was carried out in 61% of hospitalised patients, compared with 28% of non-hospitalised patients. For 2.4% of patients, the final diagnosis was organic., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

Hemodialysis is a necessary treatment for end-stage kidney disease patients. It imposes undergoing three sessions of dialysis per week in a specialized center. Amid the SARS-CoV-2 pandemic, precautionary measures were mandatory in all dialysis facilities and may have negatively impacted patients' well-being. This study aimed to uncover the scale of this effect. We performed a cross-sectional study of all patients undergoing chronic hemodialysis in two dialysis units (one urban and another rural). Patients with Alzheimer's disease were excluded. Patients filled a questionnaire including information on socio-demographics, factors related to the dialysis facility, and the impact of the COVID-19 epidemic on their mental health. A total of 72 patients responded. Their median age was 70 (60.79) years. Of them, 68% were males, 71% were married, and 10% were living alone. Following the pandemic, 35% felt more anxious, with a higher incidence of anxiety in the rural unit (p=0.021). Half of them felt very limited in their relationships, and 29% were isolated from their families. In total, 98% of patients were satisfied with the staff support. The imposed preventive measures were perceived as very strict in 27% of the surveyed patients. The majority of the urban group were bothered for not eating during the session, and they felt significantly more stress than the rural group (p=0.001). The SARS-CoV-2 pandemic increased anxiety among hemodialysis patients from a rural setting. Stress was more prevalent in the urban group and most probably related to limitations in eating during sessions. The majority were satisfied with staff support., (©2021 JOURNAL of MEDICINE and LIFE.)

Standard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently annotate behavioral features needed for accurate machine learning detection of the common childhood developmental disorder Autism Spectrum Disorder (ASD) for children under 8 years old. We implement a novel process for identifying and certifying a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from a pool of 1,107. Two previously validated ASD logistic regression classifiers, evaluated against parent-reported diagnoses, were used to assess the accuracy of the trusted crowd's ratings of unstructured home videos. A representative balanced sample (N = 50 videos) of videos were evaluated with and without face box and pitch shift privacy alterations, with AUROC and AUPRC scores > 0.98. With both privacy-preserving modifications, sensitivity is preserved (96.0%) while maintaining specificity (80.0%) and accuracy (88.0%) at levels comparable to prior classification methods without alterations. We find that machine learning classification from features extracted by a certified nonexpert crowd achieves high performance for ASD detection from natural home videos of the child at risk and maintains high sensitivity when privacy-preserving mechanisms are applied. These results suggest that privacy-safeguarded crowdsourced analysis of short home videos can help enable rapid and mobile machine-learning detection of developmental delays in children.

Augmented reality (AR) has been studied as a clinical teaching tool, however eye-tracking capabilities integrated within an AR medical simulator have limited research. The recently developed Chariot Augmented Reality Medical (CHARM) simulator integrates real-time communication into a portable medical simulator. The purpose of this project was to refine the gaze-tracking capabilities of the CHARM simulator on the Magic Leap One (ML1). Adults aged 18 years and older were recruited using convenience sampling. Participants were provided with an ML1 headset that projected a hologram of a patient, bed and monitor. They were instructed via audio recording to gaze at variables in this scenario. The participant gaze targets from the ML1 output were compared with the specified gaze points from the audio recording. A priori investigators planned to iterative modifications of the eye-tracking software until a capture rate of 80% was achieved. Two consecutive participants with a capture rate less than 80% triggered software modifications and the project concluded after three consecutive participants' capture rates were greater than 80%. Thirteen participants were included in the study. Eye-tracking concordance was less than 80% reliable in the first 10 participants. The investigators hypothesised that the eye movement detection threshold was too sensitive, thus the algorithm was adjusted to reduce noise. The project concluded after the final three participants' gaze capture rates were 80%, 80% and 80.1%, respectively. This report suggests that eye-tracking technology can be reliably used with the ML1 enabled with CHARM simulator software., Competing Interests: Competing interests: TC, EW and SR are on the board of directors for Chariot Kids, a California non-profit organisation that seeks to train providers how to use technologies in healthcare and distribute technologies to sick children., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Crowd-powered telemedicine has the potential to revolutionize healthcare, especially during times that require remote access to care. However, sharing private health data with strangers from around the world is not compatible with data privacy standards, requiring a stringent filtration process to recruit reliable and trustworthy workers who can go through the proper training and security steps. The key challenge, then, is to identify capable, trustworthy, and reliable workers through high-fidelity evaluation tasks without exposing any sensitive patient data during the evaluation process. We contribute a set of experimentally validated metrics for assessing the trustworthiness and reliability of crowd workers tasked with providing behavioral feature tags to unstructured videos of children with autism and matched neurotypical controls. The workers are blinded to diagnosis and blinded to the goal of using the features to diagnose autism. These behavioral labels are fed as input to a previously validated binary logistic regression classifier for detecting autism cases using categorical feature vectors. While the metrics do not incorporate any ground truth labels of child diagnosis, linear regression using the 3 correlative metrics as input can predict the mean probability of the correct class of each worker with a mean average error of 7.51% for performance on the same set of videos and 10.93% for performance on a distinct balanced video set with different children. These results indicate that crowd workers can be recruited for performance based largely on behavioral metrics on a crowdsourced task, enabling an affordable way to filter crowd workforces into a trustworthy and reliable diagnostic workforce.

Introduction: The new initiative by the Department of Health and Human Services (DHHS) aims to decrease new HIV infections in the U.S. by 75% within 5 years and 90% within 10 years. Our objective was to evaluate whether the U.S. military provides a good example of the benefits of such policies., Materials and Methods: We conducted an analysis of a cohort of 1,405 active duty military personnel with HIV enrolled in the Natural History Study who were diagnosed between 2003 and 2015 at six U.S. military medical centers. The study was approved by institutional review boards at the Uniformed Services University of the Health Sciences and each of the sites. We evaluated the impact of Department of Defense (DoD) HIV care policies, including screening, linkage to care, treatment eligibility, and combined antiretroviral therapy (cART) initiation on achieving viral suppression (VS) within 3 years of diagnosis. As a secondary outcome, we evaluated the DoD's achievement of UNAIDS 90-90-90 targets., Results: Nearly all (99%) were linked to care within 60 days. Among patients diagnosed in 2003-2009, 77.5% (95% confidence intervals (CI) 73.9-80.6%) became eligible for cART within 3 years of diagnosis, 70.6% (95% CI 66.6-74.1%) overall initiated cART, and 64.2% (95% CI 60.1-68.0%) overall achieved VS. Among patients diagnosed in 2010-2015, 98.7% (95% CI 96.7-99.5%) became eligible for cART within 3 years of diagnosis, 98.5% (95% CI 96.4-99.4%) overall initiated cART, and 89.8% (95% CI 86.0-92.5%) overall achieved VS., Conclusions: U.S. military HIV policies have been highly successful in achieving VS goals, exceeding the UNAIDS 90-90-90 targets. In spite of limitations, including generalizability, this example demonstrates the feasibility of the DHHS initiative to decrease new infections through testing, early treatment, and retention in care., (Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers-defined as vetted members of popular crowdsourcing platforms-to aid in the task of diagnosing autism. We evaluate workers when crowdsourcing the task of providing categorical ordinal behavioral ratings to unstructured public YouTube videos of children with autism and neurotypical controls. To evaluate emerging patterns that are consistent across independent crowds, we target workers from distinct geographic loci on two crowdsourcing platforms: an international group of workers on Amazon Mechanical Turk (MTurk) (N = 15) and Microworkers from Bangladesh (N = 56), Kenya (N = 23), and the Philippines (N = 25). We feed worker responses as input to a validated diagnostic machine learning classifier trained on clinician-filled electronic health records. We find that regardless of crowd platform or targeted country, workers vary in the average confidence of the correct diagnosis predicted by the classifier. The best worker responses produce a mean probability of the correct class above 80% and over one standard deviation above 50%, accuracy and variability on par with experts according to prior studies. There is a weak correlation between mean time spent on task and mean performance ( r = 0.358, p = 0.005). These results demonstrate that while the crowd can produce accurate diagnoses, there are intrinsic differences in crowdworker ability to rate behavioral features. We propose a novel strategy for recruitment of crowdsourced workers to ensure high quality diagnostic evaluations of autism, and potentially many other pediatric behavioral health conditions. Our approach represents a viable step in the direction of crowd-based approaches for more scalable and affordable precision medicine.

Data science and digital technologies have the potential to transform diagnostic classification. Digital technologies enable the collection of big data, and advances in machine learning and artificial intelligence enable scalable, rapid, and automated classification of medical conditions. In this review, we summarize and categorize various data-driven methods for diagnostic classification. In particular, we focus on autism as an example of a challenging disorder due to its highly heterogeneous nature. We begin by describing the frontier of data science methods for the neuropsychiatry of autism. We discuss early signs of autism as defined by existing pen-and-paper-based diagnostic instruments and describe data-driven feature selection techniques for determining the behaviors that are most salient for distinguishing children with autism from neurologically typical children. We then describe data-driven detection techniques, particularly computer vision and eye tracking, that provide a means of quantifying behavioral differences between cases and controls. We also describe methods of preserving the privacy of collected videos and prior efforts of incorporating humans in the diagnostic loop. Finally, we summarize existing digital therapeutic interventions that allow for data capture and longitudinal outcome tracking as the diagnosis moves along a positive trajectory. Digital phenotyping of autism is paving the way for quantitative psychiatry more broadly and will set the stage for more scalable, accessible, and precise diagnostic techniques in the field., (Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Despite the efforts made, the number of accidents has not significantly decreased in the construction industry. The main reasons can be found in the peculiarities of working activities in this sector, where hazard analysis and safety management are more difficult than in other industries. To deal with these problems, a comprehensive approach for hazard analysis is needed, focusing on the activities in which a working task is articulated since they are characterized by different types of hazards and thus risk levels. The study proposes a methodology that integrates quality function deployment (QFD) and analytic network process methods to correlate working activities, hazardous events and possible consequences. This provides more effective decision-making, while reducing the ambiguity of the qualitative assessment criteria. The results achieved can augment knowledge on the usability of QFD in safety research, providing a basis for its application for further studies.

Background: Several studies have shown that facial attention differs in children with autism. Measuring eye gaze and emotion recognition in children with autism is challenging, as standard clinical assessments must be delivered in clinical settings by a trained clinician. Wearable technologies may be able to bring eye gaze and emotion recognition into natural social interactions and settings., Objective: This study aimed to test: (1) the feasibility of tracking gaze using wearable smart glasses during a facial expression recognition task and (2) the ability of these gaze-tracking data, together with facial expression recognition responses, to distinguish children with autism from neurotypical controls (NCs)., Methods: We compared the eye gaze and emotion recognition patterns of 16 children with autism spectrum disorder (ASD) and 17 children without ASD via wearable smart glasses fitted with a custom eye tracker. Children identified static facial expressions of images presented on a computer screen along with nonsocial distractors while wearing Google Glass and the eye tracker. Faces were presented in three trials, during one of which children received feedback in the form of the correct classification. We employed hybrid human-labeling and computer vision-enabled methods for pupil tracking and world-gaze translation calibration. We analyzed the impact of gaze and emotion recognition features in a prediction task aiming to distinguish children with ASD from NC participants., Results: Gaze and emotion recognition patterns enabled the training of a classifier that distinguished ASD and NC groups. However, it was unable to significantly outperform other classifiers that used only age and gender features, suggesting that further work is necessary to disentangle these effects., Conclusions: Although wearable smart glasses show promise in identifying subtle differences in gaze tracking and emotion recognition patterns in children with and without ASD, the present form factor and data do not allow for these differences to be reliably exploited by machine learning systems. Resolving these challenges will be an important step toward continuous tracking of the ASD phenotype., (©Anish Nag, Nick Haber, Catalin Voss, Serena Tamura, Jena Daniels, Jeffrey Ma, Bryan Chiang, Shasta Ramachandran, Jessey Schwartz, Terry Winograd, Carl Feinstein, Dennis P Wall. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.04.2020.)