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…

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…

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…

Machine learning for early detection of autism (and other conditions) using a parental questionnaire and home video screening.

2017 IEEE International Conference on Big Data. Abstract Existing screening tools for early detection of autism are expensive, cumbersome, time-intensive, and sometimes fall short in predictive value. In this work, we apply Machine Learning to gold standard clinical data obtained across thousands of children at risk for autism spectrum disorders to create a low-cost, quick, and easy to apply autism…

Crowdsourced validation of a machine-learning classification system for autism and ADHD.

Translational Psychiatry. Abstract Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) together affect >10% of the children in the United States, but considerable behavioral overlaps between the two disorders can often complicate differential diagnosis. Currently, there is no screening test designed to differentiate between the two disorders, and with waiting times from initial suspicion to diagnosis upwards of…

Use of machine learning for behavioral distinction of autism and ADHD

Translational Psychiatry volume 6, page 732(2016) M Duda, R Ma, N Haber & D P Wall Abstract Although autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) continue to rise in prevalence, together affecting >10% of today’s pediatric population, the methods of diagnosis remain subjective, cumbersome and time intensive. With gaps upward of a year between initial suspicion and diagnosis, valuable time where treatments and behavioral…

Use of machine learning to shorten observation-based screening and diagnosis of autism.

Translational Psychiatry. Abstract The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series…

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