Living with autism without knowing: receiving a diagnosis in later life

Living with autism without knowing: receiving a diagnosis in later life

ABSTRACT
Increasingly adults over the age of 50 are receiving a diagnosis of
autism spectrum condition. Growing up in a time when autism
was poorly recognised, these adults have lived unknowingly with
the condition and face readjustment. This paper reports the first
study to investigate this population. Nine adults over the age of
50, who had recently been diagnosed with ASC, were interviewed,
and thematic analysis was used to analyse the transcripts. Results
showed that the participants had received treatment for anxiety
and depression. They reported ASC behaviours in their childhood
and growing up they felt isolated and alien. Receiving a diagnosis
was seen as a positive step and allowed for a reconfiguration of
self and an appreciation of individual needs. Given the positive
aspects of receiving a late diagnosis, more work is needed to
identify older adults with undiagnosed ASC

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Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study

Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study

Abstract
Background: Autism spectrum disorder (ASD) is currently diagnosed using qualitative methods that measure between 20-100 behaviors, can span multiple appointments with trained clinicians, and take several hours to complete. In our previous work, we demonstrated the efficacy of machine learning classifiers to accelerate the process by collecting home videos of US-based children,
identifying a reduced subset of behavioral features that are scored by untrained raters using a machine learning classifier to determine children’s “risk scores” for autism. We achieved an accuracy of 92% (95% CI 88%-97%) on US videos using a classifier built on five features.

Objective: Using videos of Bangladeshi children collected from Dhaka Shishu Children’s Hospital, we aim to scale our pipeline to another culture and other developmental delays, including speech and language conditions.

Methods: Although our previously published and validated pipeline and set of classifiers perform reasonably well on Bangladeshi videos (75% accuracy, 95% CI 71%-78%), this work improves on that accuracy through the development and application of a powerful new technique for adaptive aggregation of crowdsourced labels. We enhance both the utility and performance of our
model by building two classification layers: The first layer distinguishes between typical and atypical behavior, and the second layer distinguishes between ASD and non-ASD. In each of the layers, we use a unique rater weighting scheme to aggregate classification scores from different raters based on their expertise. We also determine Shapley values for the most important features in the classifier to understand how the classifiers’ process aligns with clinical intuition.

Results: Using these techniques, we achieved an accuracy (area under the curve [AUC]) of 76% (SD 3%) and sensitivity of 76% (SD 4%) for identifying atypical children from among developmentally delayed children, and an accuracy (AUC) of 85% (SD 5%) and sensitivity of 76% (SD 6%) for identifying children with ASD from those predicted to have other developmental
delays

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