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

Deveau, Nicholas D.; Washington, Peter; Leblanc, Émilie; Husic, Arman; Dunlap, Kaitlyn; Penev, Yordan; Kline, Aaron; Mutlu, Onur Cezmi; Wall, Dennis P.

doi:10.1016/j.ibmed.2022.100057

Cited by 13 publications

(9 citation statements)

References 48 publications

(57 reference statements)

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“…A total of 4 studies involving 4 unique apps were included in the review ( Figure 2 ), of which 1 (Canvas Dx [ 16 ]) was an FDA-cleared commercial product and 3 (Guess What? [ 17 ], START [ 18 ], and SenseToKnow [ 19 ]) were research apps.…”

Section: Resultsmentioning

confidence: 99%

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

Ponzo,

May,

Tamayo-Elizalde

et al. 2023

JMIR Mhealth Uhealth

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Background Diagnostic delays in autism are common, with the time to diagnosis being up to 3 years from the onset of symptoms. Such delays have a proven detrimental effect on individuals and families going through the process. Digital health products, such as mobile apps, can help close this gap due to their scalability and ease of access. Further, mobile apps offer the opportunity to make the diagnostic process faster and more accurate by providing additional and timely information to clinicians undergoing autism assessments. Objective The aim of this scoping review was to synthesize the available evidence about digital biomarker tools to aid clinicians, researchers in the autism field, and end users in making decisions as to their adoption within clinical and research settings. Methods We conducted a structured literature search on databases and search engines to identify peer-reviewed studies and regulatory submissions that describe app characteristics, validation study details, and accuracy and validity metrics of commercial and research digital biomarker apps aimed at aiding the diagnosis of autism. Results We identified 4 studies evaluating 4 products: 1 commercial and 3 research apps. The accuracy of the identified apps varied between 28% and 80.6%. Sensitivity and specificity also varied, ranging from 51.6% to 81.6% and 18.5% to 80.5%, respectively. Positive predictive value ranged from 20.3% to 76.6%, and negative predictive value fluctuated between 48.7% and 97.4%. Further, we found a lack of details around participants’ demographics and, where these were reported, important imbalances in sex and ethnicity in the studies evaluating such products. Finally, evaluation methods as well as accuracy and validity metrics of available tools were not clearly reported in some cases and varied greatly across studies. Different comparators were also used, with some studies validating their tools against the Diagnostic and Statistical Manual of Mental Disorders criteria and others through self-reported measures. Further, while in most cases, 2 classes were used for algorithm validation purposes, 1 of the studies reported a third category (indeterminate). These discrepancies substantially impact the comparability and generalizability of the results, thus highlighting the need for standardized validation processes and the reporting of findings. Conclusions Despite their popularity, systematic evaluations and syntheses of the current state of the art of digital health products are lacking. Standardized and transparent evaluations of digital health tools in diverse populations are needed to assess their real-world usability and validity, as well as help researchers, clinicians, and end users safely adopt novel tools within clinical and research practices.

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Section: Resultsmentioning

confidence: 99%

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

Ponzo,

May,

Tamayo-Elizalde

et al. 2023

JMIR Mhealth Uhealth

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“…DTx have shown promise in various disease areas including psychiatric, [36][37][38] cardiovascular, 29,39 metabolism, 40 gastrointestinal, 41 neurodevelopmental, 33,42 and neurological diseases, 43,44 among others. With such a broad application, DTx hold great potential in translatability and treatment across different disease areas among the general population.…”

Section: Mechanism Of Action Potential and Applications Of Dtxmentioning

confidence: 99%

“… 27 , 28 The software can be further divided into subtypes such as mobile applications, 29 web applications, 30 , 31 virtual reality (VR), 32 AI, 33 , 34 video games, 35 and approved devices, among others. DTx have shown promise in various disease areas including psychiatric, 36 , 37 , 38 cardiovascular, 29 , 39 metabolism, 40 gastrointestinal, 41 neurodevelopmental, 33 , 42 and neurological diseases, 43 , 44 among others. With such a broad application, DTx hold great potential in translatability and treatment across different disease areas among the general population.…”

Section: Mechanism Of Action Potential and Applications Of Dt ...mentioning

confidence: 99%

Digital therapeutics in the clinic

Phan

Mitragotri

Zhao

2023

Bioengineering & Transla Med

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Digital therapeutics are emerging as a new form of therapeutic interventions. Unlike conventional therapeutics, digital therapeutics deliver interventions directly to patients using an evidence‐based, clinically evaluated software to treat, manage, or prevent diseases. Digital therapeutics manifest in diverse forms such as web‐based applications, mobile applications on smart devices, virtual reality, and video games. As its own product category for FDA approval, digital therapeutics can function as stand‐alone treatments or in combination with conventional therapeutics to improve adherence and/or efficacy. Here, we review the clinical landscape of digital therapeutics. We summarize FDA‐approved products and their clinical use, overview >300 ongoing clinical trials, and discuss challenges for their clinical translation and strategies to overcome the same.

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“…The field of digital phenotyping is vast and broad. A non-exhaustive list of NIHfunded projects for developmental diagnostics include Guillermo Sapiro's work (R01MH120093) developing active closed-loop data collection for gaze and motor features for ASD as well as ADHD [11][12][13][14][15][16][17][18], James Rehg's work (R01MH114999) modeling nonverbal communication in atypical and typical development [19][20], Robert Schultz's work (R01MH118327) involving diagnostic computer vision analyses of motor movements displayed in videos of dyadic social interactions involving children with ASD [21], and Dennis Wall's work (R01LM013364) exploring the use of mobile games to acquire computer vision data for deep learning prediction of individual ASD-related behaviors [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

Digitally Diagnosing Multiple Developmental Delays using Crowdsourcing fused with Machine Learning: A Research Protocol

Washington

2023

Preprint

Self Cite

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Background: Roughly 17% percent of minors in the United States aged 3 through 17 years have a diagnosis of one or more developmental or psychiatric conditions, with the true prevalence likely being higher due to underdiagnosis in rural areas and for minority populations. Unfortunately, timely diagnostic services are inaccessible to a large portion of the United States and global population due to cost, distance, and clinician availability. Digital phenotyping tools have the potential to shorten the time-to-diagnosis and to bring diagnostic services to more people by enabling accessible evaluations. While automated machine learning (ML) approaches for detection of pediatric psychiatry conditions have garnered increased research attention in recent years, existing approaches use a limited set of social features for the prediction task and focus on a single binary prediction. Objective: I propose the development of a gamified web system for data collection followed by a fusion of novel crowdsourcing algorithms with machine learning behavioral feature extraction approaches to simultaneously predict diagnoses of Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) in a precise and specific manner. Methods: The proposed pipeline will consist of: (1) a gamified web applications to curate videos of social interactions adaptively based on needs of the diagnostic system, (2) behavioral feature extraction techniques consisting of automated ML methods and novel crowdsourcing algorithms, and (3) development of ML models which classify several conditions simultaneously and which adaptively request additional information based on uncertainties about the data. Conclusions: The prospective for high reward stems from the possibility of creating the first AI-powered tool which can identify complex social behaviors well enough to distinguish conditions with nuanced differentiators such as ASD and ADHD.

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

Cited by 13 publications

References 48 publications

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

Digital therapeutics in the clinic

Digitally Diagnosing Multiple Developmental Delays using Crowdsourcing fused with Machine Learning: A Research Protocol

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