Multi-modular AI Approach to Streamline Autism Diagnosis in Young Children

Halim Abbas, Ford Garberson, Stuart Liu-Mayo, Eric Glover & Dennis P. Wall  Scientific Reports volume 10, Article number: 5014 (2020) Cite this article 2 Altmetric | Metricsdetails Abstract Autism has become a pressing healthcare challenge. The instruments used to aid diagnosis are time and labor expensive and require trained clinicians to administer, leading to long wait times for at-risk children. We present a multi-modular, machine learning-based assessment of autism comprising three complementary modules…

ASD symptoms in toddlers and preschoolers: An examination of sex differences

Rosmary Ros‐Demarize, Catherine Bradley, Stephen M. Kanne, Zachary Warren, Andrea Boan, Clara Lajonchere, Justine Park, Laura Arnstein Carpenter Abstract Although considerable work has documented higher prevalence rates of autism spectrum disorder (ASD) in boys, fewer studies have focused on sex differences within samples of young children at‐risk for ASD. This study examined sex differences in ASD symptom domains and ASD screening outcomes…

In-Home Speech and Language Screening for Young Children: A Proof-of-Concept Study Using Interactive Mobile Storytime

Du Y1,2, Abbas H2, Taraman S1,2,3, Segar S2, Bischoff N2. Author information 1University of California, Irvine, CA, USA.2Cognoa Inc., Palo Alto, CA, USA.3Children’s Hospital of Orange County, Orange, CA, USA. Abstract Early identification and intervention of speech and language delays in children contribute to better communication and literacy skills for school readiness and are protective against behavioral and mental health problems. Through collaboration between…

When Are We Sure? Predictors of Clinician Certainty in the Diagnosis of Autism Spectrum Disorder.

Journal of Autism and Developmental Disorders. Abstract Differential diagnosis of autism spectrum disorder (ASD) is challenging, and uncertainty regarding a child’s diagnosis may result in under-identification or prolonged diagnostic pathways. The current study examined diagnostic certainty, or how sure clinicians were that their diagnosis was accurate, among 478 toddler and preschool-aged children referred for possible ASD to academic medical specialty…

Effect of Wearable Digital Intervention for Improving Socialization in Children With Autism Spectrum Disorder: A Randomized Clinical Trial

JAMA Pediatr. 2019;173(5):446-454. doi:10.1001/jamapediatrics.2019.0285 Catalin Voss, MS1; Jessey Schwartz, BA2; Jena Daniels, BS2; et al Abstract Importance  Autism behavioral therapy is effective but expensive and difficult to access. While mobile technology–based therapy can alleviate wait-lists and scale for increasing demand, few clinical trials exist to support its use for autism spectrum disorder (ASD) care. Objective  To evaluate the efficacy of Superpower Glass, an artificial intelligence–driven wearable behavioral intervention for improving social outcomes…

Mobile detection of autism through machine learning on home video: A development and prospective validation study.

Plos Medicine. Published: November 27, 2018. Abstract Background The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access to therapy. We hypothesize that the use of machine learning analysis on home video…

Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism.

npj Digital Medicine. Published: 02 August 2018. Abstract Although standard behavioral interventions for autism spectrum disorder (ASD) are effective therapies for social deficits, they face criticism for being time-intensive and overdependent on specialists. Earlier starting age of therapy is a strong predictor of later success, but waitlists for therapies can be 18 months long. To address these complications, we developed Superpower…

Screening in toddlers and preschoolers at risk for autism spectrum disorder: Evaluating a novel mobile‐health screening tool.

Wiley Online Library. Abstract There are many available tools with varying levels of accuracy designed to screen for Autism Spectrum Disorder (ASD) in young children, both in the general population and specifically among those referred for developmental concerns. With burgeoning waitlists for comprehensive diagnostic ASD assessments, finding accurate methods and tools for advancing diagnostic triage becomes increasingly important. The current…

Machine learning approach for early detection of autism by combining questionnaire and home video screening.

JAMIA. Abstract Background Existing screening tools for early detection of autism are expensive, cumbersome, time- intensive, and sometimes fall short in predictive value. In this work, we sought to apply Machine Learning (ML) to gold standard clinical data obtained across thousands of children at-risk for autism spectrum disorder to create a low-cost, quick, and easy to apply autism screening tool.…

Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism.

Molecular Autism. Abstract Background Autism spectrum disorder (ASD) diagnosis can be delayed due in part to the time required for administration of standard exams, such as the Autism Diagnostic Observation Schedule (ADOS). Shorter and potentially mobilized approaches would help to alleviate bottlenecks in the healthcare system. Previous work using machine learning suggested that a subset of the behaviors measured by…

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