Machine learning models using mobile game play accurately classify children with autism.

Digitally-delivered healthcare is well suited to address current inequities in the delivery of care due to barriers of access to healthcare facilities. As the COVID-19 pandemic phases out, we have a unique opportunity to capitalize on the current familiarity with telemedicine approaches and continue to advocate for mainstream adoption of remote care delivery. In this paper, we specifically focus on the ability of GuessWhat? a smartphone-based charades-style gamified therapeutic intervention for autism spectrum disorder (ASD) to generate a signal that distinguishes children with ASD from neurotypical (NT) children.

Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels.

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.

Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections.

Artificial Intelligence (A.I.) solutions are increasingly considered for telemedicine.

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study.

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.

Identification of Social Engagement Indicators Associated With Autism Spectrum Disorder Using a Game-Based Mobile App: Comparative Study of Gaze Fixation and Visual Scanning Methods.

Background: Autism spectrum disorder (ASD) is a widespread neurodevelopmental condition with a range of potential causes and symptoms. Standard diagnostic mechanisms for ASD, which involve lengthy parent questionnaires and clinical observation, often result in long waiting times for results. Recent advances in computer vision and mobile technology hold potential for speeding up the diagnostic process by enabling computational analysis of behavioral and social impairments from home videos.

A Mobile Game Platform for Improving Social Communication in Children with Autism: A Feasibility Study.

Background: Many children with autism cannot receive timely in-person diagnosis and therapy, especially in situations where access is limited by geography, socioeconomics, or global health concerns such as the current COVD-19 pandemic. Mobile solutions that work outside of traditional clinical environments can safeguard against gaps in access to quality care.

Objective: The aim of the study is to examine the engagement level and therapeutic feasibility of a mobile game platform for children with autism.

Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection.

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.

Feature replacement methods enable reliable home video analysis for machine learning detection of autism.

Autism Spectrum Disorder is a neuropsychiatric condition affecting 53 million children worldwide and for which early diagnosis is critical to the outcome of behavior therapies. Machine learning applied to features manually extracted from readily accessible videos (e.g.

Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition.

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.

Making sense of what exactly public health does: a typology of public health interventions.

Setting: Montréal.

Intervention: The lack of common knowledge about what public health does is a hindrance to its recognition and capacity to act. Montréal's regional public health department set an explicit goal to clarify and better communicate its specific contributions when it developed its 2016-2021 action plan.

Influence of a Pay-for-Performance Program on Glycemic Control in Patients Living with Diabetes by Family Physicians in a Canadian Province.

Objectives: We evaluated the influence of the introduction of a pay-for-performance program implemented in 2010 for family physicians on the glycemic control of patients with diabetes.

Methods: Administrative data for all 583 eligible family physicians and 83,580 adult patients with diabetes in New Brunswick over 10 years were used. We compared the probability of receiving at least 2 tests for glycated hemoglobin (A1C) levels and achieving glycemic control before (2005-2009) and after (2010-2014) the implementation of the program and between patients divided based on whether a physician claimed the incentive or did not.

Factors that could explain the increasing prevalence of type 2 diabetes among adults in a Canadian province: a critical review and analysis.

Background: The prevalence of diabetes has increased since the last decade in New Brunswick. Identifying factors contributing to the increase in diabetes prevalence will help inform an action plan to manage the condition. The objective was to describe factors that could explain the increasing prevalence of type 2 diabetes in New Brunswick since 2001.

How do selected arrows guide visuospatial attention? Dissociating symbolic value and spatial proximity.

Previous research on the control of visuospatial attention showed that overlearned symbols like arrows have the potential to induce involuntary shifts of attention. Following work on the role of attentional control settings and of the content of working memory in the involuntary deployment of visuospatial attention, Pratt and Hommel (2003) found that this unintentional orienting by an arrow depended on its top-down selection, contingent on the attentional control settings, that is to say, the target selection cue. However, in this study, each arrow was closer to the location it indicated than to any other location, raising the issue of attention being drawn to the arrow location, facilitating processing at adjacent locations, rather than pushed to the symbolically cued location.

Contingent capture of visual-spatial attention depends on capacity-limited central mechanisms: evidence from human electrophysiology and the psychological refractory period.

It has recently been demonstrated that a lateralized distractor that matches the individual's top-down control settings elicits an N2pc wave, an electrophysiological index of the focus of visual-spatial attention, indicating that contingent capture has a visual-spatial locus. Here, we investigated whether contingent capture required capacity-limited central resources by incorporating a contingent capture task as the second task of a psychological refractory period (PRP) dual-task paradigm. The N2pc was used to monitor where observers were attending while they performed concurrent central processing known to cause the PRP effect.

Tracking the location of visuospatial attention in a contingent capture paradigm.

Currently, there is considerable controversy regarding the degree to which top-down control can affect attentional capture by salient events. According to the contingent capture hypothesis, attentional capture by a salient stimulus is contingent on a match between the properties of the stimulus and top-down attentional control settings. In contrast, bottom-up saliency accounts argue that the initial capture of attention is determined solely by the relative salience of the stimulus, and the effect of top-down attentional control is limited to effects on the duration of attentional engagement on the capturing stimulus.

The time course of the contingent spatial blink.

Attentional capture is the unintentional deployment of attention to a task-irrelevant but attentionally salient object. The contingent involuntary orienting hypothesis states that it occurs only if a distractor's property matches current top-down attentional control settings (Folk, Remington, & Johnston, 1992). Folk, Leber, and Egeth (2002) found that monitoring a central RSVP stream for a coloured target led to spatial attentional capture by a peripheral distractor that matched the target colour.